In [1]:
%run ADL_sea.ipynb
Number of input: 3 Number of output: 2 Number of batch: 200 All labeled
100% (200 of 200) |######################| Elapsed Time: 0:02:14 ETA: 00:00:00
=== Performance result === Accuracy: 92.47537688442212 (+/-) 6.88610477180749 Testing Loss: 0.2420973918077784 (+/-) 0.174566812425013 Precision: 0.9252151888619986 Recall: 0.9247537688442211 F1 score: 0.9240104512920709 Testing Time: 0.0026751295406015675 (+/-) 0.003450418901497672 Training Time: 0.6739132631963222 (+/-) 0.04991744609300713 === Average network evolution === Total hidden node: 11.557788944723619 (+/-) 4.318410643757607 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=19, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=19, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 19 No. of parameters : 116 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:02:20 ETA: 00:00:00
=== Performance result === Accuracy: 92.39899497487438 (+/-) 7.31767317516075 Testing Loss: 0.24268003636456315 (+/-) 0.17876836341987254 Precision: 0.9245291800410059 Recall: 0.9239899497487437 F1 score: 0.9232049062219255 Testing Time: 0.002700068842825578 (+/-) 0.003790747152800348 Training Time: 0.6994425327933613 (+/-) 0.07097746397496776 === Average network evolution === Total hidden node: 10.537688442211055 (+/-) 4.302311717435989 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=18, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=18, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 18 No. of parameters : 110 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:02:22 ETA: 00:00:00
=== Performance result === Accuracy: 92.21608040201005 (+/-) 7.6784985127706396 Testing Loss: 0.2438871257344083 (+/-) 0.18202602448016147 Precision: 0.922751258284536 Recall: 0.9221608040201005 F1 score: 0.9213232590133877 Testing Time: 0.002500949792526475 (+/-) 0.0024791113219701853 Training Time: 0.7138597078658827 (+/-) 0.06081365743068822 === Average network evolution === Total hidden node: 11.773869346733669 (+/-) 4.966634463344113 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 20 No. of parameters : 122 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:02:23 ETA: 00:00:00
=== Performance result === Accuracy: 92.45427135678392 (+/-) 7.024828140155979 Testing Loss: 0.2457714871639133 (+/-) 0.17994961621511688 Precision: 0.924898421882622 Recall: 0.9245427135678392 F1 score: 0.9238355155454488 Testing Time: 0.0027041926455857166 (+/-) 0.003274716189284338 Training Time: 0.715763185491514 (+/-) 0.03692923894375927 === Average network evolution === Total hidden node: 11.608040201005025 (+/-) 4.432308934499736 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=19, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=19, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 19 No. of parameters : 116 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:02:15 ETA: 00:00:00
=== Performance result === Accuracy: 92.37688442211055 (+/-) 7.188241958502679 Testing Loss: 0.2415224832094195 (+/-) 0.17916739408981872 Precision: 0.9240753575339399 Recall: 0.9237688442211055 F1 score: 0.9230677109809373 Testing Time: 0.0024214629551873135 (+/-) 0.00301272470123567 Training Time: 0.674902778175009 (+/-) 0.04646547160194004 === Average network evolution === Total hidden node: 11.85427135678392 (+/-) 5.354176442143243 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 20 No. of parameters : 122 Voting weight: [1.0]
Mean Accuracy: 92.52585858585859 Std Accuracy: 6.96149569005313 Hidden Node mean 11.495959595959595 Hidden Node std: 4.710774651429352 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (200 of 200) |######################| Elapsed Time: 0:01:06 ETA: 00:00:00
=== Performance result === Accuracy: 90.78391959798995 (+/-) 9.749533822823517 Testing Loss: 0.2673834305275325 (+/-) 0.18655504090906838 Precision: 0.9103782945359641 Recall: 0.9078391959798995 F1 score: 0.9060898386562237 Testing Time: 0.0023695643822751454 (+/-) 0.0035019492575508343 Training Time: 0.32802253152856875 (+/-) 0.019634724509234057 === Average network evolution === Total hidden node: 6.562814070351759 (+/-) 3.979195156191552 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=14, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=14, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 14 No. of parameters : 86 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:01:07 ETA: 00:00:00
=== Performance result === Accuracy: 90.52864321608038 (+/-) 9.134326096352938 Testing Loss: 0.27593131022686934 (+/-) 0.1839924027924849 Precision: 0.9072036494542475 Recall: 0.9052864321608041 F1 score: 0.9036280131122092 Testing Time: 0.002502805623576869 (+/-) 0.0037284154085801035 Training Time: 0.33625364303588867 (+/-) 0.025852237269212326 === Average network evolution === Total hidden node: 6.673366834170854 (+/-) 3.1075922947609795 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 13 No. of parameters : 80 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:01:10 ETA: 00:00:00
=== Performance result === Accuracy: 90.48341708542713 (+/-) 10.193745071110198 Testing Loss: 0.27469674441682634 (+/-) 0.1932502877801274 Precision: 0.9067718159107777 Recall: 0.9048341708542713 F1 score: 0.9031563547332235 Testing Time: 0.00270002930607628 (+/-) 0.0038024585217603067 Training Time: 0.35018755802557094 (+/-) 0.02417822682621283 === Average network evolution === Total hidden node: 9.256281407035177 (+/-) 4.094321136531829 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=17, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=17, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 17 No. of parameters : 104 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:01:07 ETA: 00:00:00
=== Performance result === Accuracy: 91.20904522613064 (+/-) 8.16900125477204 Testing Loss: 0.2695008166246678 (+/-) 0.17643954343959203 Precision: 0.9135044486743105 Recall: 0.9120904522613066 F1 score: 0.9107668854219414 Testing Time: 0.00265337234765441 (+/-) 0.003954456923554642 Training Time: 0.3335821053490567 (+/-) 0.019540475833703504 === Average network evolution === Total hidden node: 8.14572864321608 (+/-) 3.4805806346795354 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=15, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=15, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 15 No. of parameters : 92 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:01:06 ETA: 00:00:00
=== Performance result === Accuracy: 91.33467336683417 (+/-) 7.45389539446337 Testing Loss: 0.2715872459151038 (+/-) 0.16798782376932672 Precision: 0.9140176915452691 Recall: 0.9133467336683417 F1 score: 0.9122965392272976 Testing Time: 0.002801953847683854 (+/-) 0.003934641437488994 Training Time: 0.3316702207728247 (+/-) 0.025133046721970302 === Average network evolution === Total hidden node: 14.698492462311558 (+/-) 3.7105780799127666 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=21, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=21, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 21 No. of parameters : 128 Voting weight: [1.0]
N/A% (0 of 200) | | Elapsed Time: 0:00:00 ETA: --:--:--
Mean Accuracy: 91.02020202020202 Std Accuracy: 8.719952504249067 Hidden Node mean 9.082828282828283 Hidden Node std: 4.753011600400689 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (200 of 200) |######################| Elapsed Time: 0:00:35 ETA: 00:00:00
=== Performance result === Accuracy: 88.96381909547738 (+/-) 10.76065312916779 Testing Loss: 0.3043554661861017 (+/-) 0.18843773360041083 Precision: 0.8946260321476117 Recall: 0.8896381909547739 F1 score: 0.8865929888749416 Testing Time: 0.0022217412689822403 (+/-) 0.0037598725181260074 Training Time: 0.17327104141963787 (+/-) 0.01738355145084289 === Average network evolution === Total hidden node: 4.442211055276382 (+/-) 2.718894827146466 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=10, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=10, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 10 No. of parameters : 62 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:00:36 ETA: 00:00:00
=== Performance result === Accuracy: 87.50351758793968 (+/-) 11.914803835592318 Testing Loss: 0.32693379829127583 (+/-) 0.2012860146787662 Precision: 0.8808548967987914 Recall: 0.8750351758793969 F1 score: 0.8710439299665473 Testing Time: 0.0021707316738876267 (+/-) 0.0031799967854169208 Training Time: 0.18048469744735027 (+/-) 0.017121671889080575 === Average network evolution === Total hidden node: 5.42713567839196 (+/-) 2.29998193933993 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 9 No. of parameters : 56 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:00:35 ETA: 00:00:00
=== Performance result === Accuracy: 88.9286432160804 (+/-) 11.231413844797423 Testing Loss: 0.30603738147859 (+/-) 0.1938833172955363 Precision: 0.8939750750368572 Recall: 0.889286432160804 F1 score: 0.8863000789115422 Testing Time: 0.0022210595595776733 (+/-) 0.00275415620605113 Training Time: 0.1719920982667549 (+/-) 0.013481649668472503 === Average network evolution === Total hidden node: 7.21608040201005 (+/-) 3.0602891303559394 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 13 No. of parameters : 80 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:00:33 ETA: 00:00:00
=== Performance result === Accuracy: 89.22110552763817 (+/-) 9.428735975431254 Testing Loss: 0.3055427845324104 (+/-) 0.17419639722515848 Precision: 0.8960722811974832 Recall: 0.8922110552763819 F1 score: 0.8895626203933883 Testing Time: 0.0019633829893179276 (+/-) 0.0028245622885234144 Training Time: 0.1628216151616082 (+/-) 0.010683908191211037 === Average network evolution === Total hidden node: 6.814070351758794 (+/-) 1.9723217996774234 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=11, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=11, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 11 No. of parameters : 68 Voting weight: [1.0]
100% (200 of 200) |######################| Elapsed Time: 0:00:33 ETA: 00:00:00
=== Performance result === Accuracy: 87.27939698492462 (+/-) 11.447671095280452 Testing Loss: 0.3218272822211735 (+/-) 0.19539244023833321 Precision: 0.88193266430969 Recall: 0.8727939698492462 F1 score: 0.8678313319880798 Testing Time: 0.001976243215589667 (+/-) 0.0029755320140556914 Training Time: 0.16283929048471116 (+/-) 0.010076730269205683 === Average network evolution === Total hidden node: 4.567839195979899 (+/-) 2.6246169099300585 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=10, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=10, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 10 No. of parameters : 62 Voting weight: [1.0]
Mean Accuracy: 88.5 Std Accuracy: 10.915561128383404 Hidden Node mean 5.702020202020202 Hidden Node std: 2.806395286598519 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
94% (188 of 200) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 63.03636363636364 (+/-) 7.620592179135286 Testing Loss: 0.6486203345385465 (+/-) 0.036306491302862384 Precision: 0.3973583140495867 Recall: 0.6303636363636363 F1 score: 0.4874474690025041 Testing Time: 0.0015305690091065687 (+/-) 0.0033436282702815363 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 5 No. of parameters : 32 Voting weight: [1.0]
91% (183 of 200) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 72.82222222222222 (+/-) 5.881584950196236 Testing Loss: 0.5691407699175556 (+/-) 0.03156485015610682 Precision: 0.7592566887363517 Recall: 0.7282222222222222 F1 score: 0.6884780357252303 Testing Time: 0.0013918190291433623 (+/-) 0.002365142297307859 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 6.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 6 No. of parameters : 38 Voting weight: [1.0]
95% (190 of 200) |#################### | Elapsed Time: 0:00:00 ETA: 0:00:00
=== Performance result === Accuracy: 63.66464646464646 (+/-) 7.618151014190566 Testing Loss: 0.5589947190248605 (+/-) 0.06280817529548549 Precision: 0.7542900127698696 Recall: 0.6366464646464647 F1 score: 0.5021764906454884 Testing Time: 0.0013549857669406468 (+/-) 0.00272378270729878 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 4 No. of parameters : 26 Voting weight: [1.0]
99% (198 of 200) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 63.03636363636364 (+/-) 7.620592179135286 Testing Loss: 0.6580547112407107 (+/-) 0.03950826087293183 Precision: 0.3973583140495867 Recall: 0.6303636363636363 F1 score: 0.4874474690025041 Testing Time: 0.0015402899848090278 (+/-) 0.0024318230902579774 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 3.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 3 No. of parameters : 20 Voting weight: [1.0]
90% (180 of 200) |################### | Elapsed Time: 0:00:00 ETA: 0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 63.03636363636364 (+/-) 7.620592179135286 Testing Loss: 0.6202581007071216 (+/-) 0.062000340626009634 Precision: 0.3973583140495867 Recall: 0.6303636363636363 F1 score: 0.4874474690025041 Testing Time: 0.0014716278422962535 (+/-) 0.0026561803544853076 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 5 No. of parameters : 32 Voting weight: [1.0]
Mean Accuracy: 65.11695431472081 Std Accuracy: 8.279599224155337 Hidden Node mean 4.6 Hidden Node std: 1.019803902718557 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [2]:
%run ADL_hyperplane.ipynb
Number of input: 4 Number of output: 2 Number of batch: 240 All labeled
100% (240 of 240) |######################| Elapsed Time: 0:02:36 ETA: 00:00:00
=== Performance result === Accuracy: 91.61924686192468 (+/-) 5.382332583117983 Testing Loss: 0.2994545329689481 (+/-) 0.07708712246012778 Precision: 0.9162176533706246 Recall: 0.9161924686192469 F1 score: 0.9161907058544578 Testing Time: 0.0024280777536176737 (+/-) 0.0032747757773265093 Training Time: 0.6493809592274942 (+/-) 0.012592105367956111 === Average network evolution === Total hidden node: 2.430962343096234 (+/-) 0.4952108661259746 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 3 No. of parameters : 23 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:02:37 ETA: 00:00:00
=== Performance result === Accuracy: 92.12217573221756 (+/-) 5.572148488763887 Testing Loss: 0.28600116791086716 (+/-) 0.07643516977094293 Precision: 0.921232767842197 Recall: 0.9212217573221757 F1 score: 0.9212215337456449 Testing Time: 0.0023531005971102535 (+/-) 0.0034522620791369602 Training Time: 0.6532286041451298 (+/-) 0.020995350565822118 === Average network evolution === Total hidden node: 2.589958158995816 (+/-) 0.4918409596913249 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 3 No. of parameters : 23 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:02:41 ETA: 00:00:00
=== Performance result === Accuracy: 91.64100418410041 (+/-) 6.137107846165298 Testing Loss: 0.30302848732371707 (+/-) 0.07915096495004463 Precision: 0.9164348373973619 Recall: 0.9164100418410042 F1 score: 0.9164092702057459 Testing Time: 0.002542788014751099 (+/-) 0.0034965857839544456 Training Time: 0.6693316304035266 (+/-) 0.03165348960391427 === Average network evolution === Total hidden node: 4.644351464435147 (+/-) 0.4787093635134252 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:02:40 ETA: 00:00:00
=== Performance result === Accuracy: 92.10794979079496 (+/-) 3.55912105140678 Testing Loss: 0.2987334580476314 (+/-) 0.054023127573923094 Precision: 0.9210825329045311 Recall: 0.9210794979079497 F1 score: 0.9210795055180077 Testing Time: 0.0025220346251292208 (+/-) 0.0032398871077608145 Training Time: 0.6686275354489123 (+/-) 0.04634983655650105 === Average network evolution === Total hidden node: 5.171548117154812 (+/-) 0.6844553990520008 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:02:59 ETA: 00:00:00
=== Performance result === Accuracy: 92.0970711297071 (+/-) 5.428018104525603 Testing Loss: 0.2901938641794556 (+/-) 0.0716536076580636 Precision: 0.9209809247027151 Recall: 0.9209707112970711 F1 score: 0.920969935793622 Testing Time: 0.002857000757959597 (+/-) 0.0048643768264168274 Training Time: 0.7466373882533117 (+/-) 0.07452561989378949 === Average network evolution === Total hidden node: 2.0460251046025104 (+/-) 0.2928272503250659 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=2, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=2, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 2 No. of parameters : 16 Voting weight: [1.0]
Mean Accuracy: 92.06773109243699 Std Accuracy: 4.763243446368177 Hidden Node mean 3.3747899159663866 Hidden Node std: 1.3716345207035683 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (240 of 240) |######################| Elapsed Time: 0:01:28 ETA: 00:00:00
=== Performance result === Accuracy: 89.60502092050208 (+/-) 8.689548855331084 Testing Loss: 0.3280997356735014 (+/-) 0.10045905049061794 Precision: 0.8960511485376287 Recall: 0.8960502092050209 F1 score: 0.8960502569570513 Testing Time: 0.00269587369144711 (+/-) 0.004033753233156096 Training Time: 0.36741442061867174 (+/-) 0.044003626425751625 === Average network evolution === Total hidden node: 5.372384937238493 (+/-) 0.7649698235243142 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 6 No. of parameters : 44 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:01:27 ETA: 00:00:00
=== Performance result === Accuracy: 89.66861924686192 (+/-) 8.908665952647674 Testing Loss: 0.32853061291213814 (+/-) 0.10377740946932923 Precision: 0.8966885309187882 Recall: 0.8966861924686192 F1 score: 0.8966858335654025 Testing Time: 0.002634387634788098 (+/-) 0.0035627971433064226 Training Time: 0.3630629503577324 (+/-) 0.03916672000436552 === Average network evolution === Total hidden node: 3.1673640167364017 (+/-) 0.8061101374404644 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 4 No. of parameters : 30 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:01:19 ETA: 00:00:00
=== Performance result === Accuracy: 90.35146443514644 (+/-) 6.294686936575027 Testing Loss: 0.3367532365117612 (+/-) 0.08452332883426067 Precision: 0.9035486193480714 Recall: 0.9035146443514644 F1 score: 0.9035119297376629 Testing Time: 0.002540052685278729 (+/-) 0.003186597107329377 Training Time: 0.3291768119923739 (+/-) 0.013842045676363228 === Average network evolution === Total hidden node: 4.640167364016737 (+/-) 1.3398532257489828 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 6 No. of parameters : 44 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:01:19 ETA: 00:00:00
=== Performance result === Accuracy: 88.02594142259413 (+/-) 9.334427921185933 Testing Loss: 0.3605628171974645 (+/-) 0.12025257311362389 Precision: 0.8803014432454552 Recall: 0.8802594142259415 F1 score: 0.880255098053841 Testing Time: 0.0024827324695666964 (+/-) 0.003224533863789907 Training Time: 0.32883843816972674 (+/-) 0.013912471906591506 === Average network evolution === Total hidden node: 2.271966527196653 (+/-) 0.4449727354358299 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=2, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=2, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 2 No. of parameters : 16 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:01:24 ETA: 00:00:00
=== Performance result === Accuracy: 89.58995815899581 (+/-) 9.388762066232408 Testing Loss: 0.331794539193728 (+/-) 0.10771929103267788 Precision: 0.8964049703202955 Recall: 0.8958995815899582 F1 score: 0.8958635268390198 Testing Time: 0.0028289102610185054 (+/-) 0.0049145376587710135 Training Time: 0.34681823662634176 (+/-) 0.02145336973330533 === Average network evolution === Total hidden node: 4.2301255230125525 (+/-) 0.6607435149981983 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
N/A% (0 of 240) | | Elapsed Time: 0:00:00 ETA: --:--:--
Mean Accuracy: 89.62268907563025 Std Accuracy: 8.213634258162184 Hidden Node mean 3.938655462184874 Hidden Node std: 1.3916080562191462 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (240 of 240) |######################| Elapsed Time: 0:00:41 ETA: 00:00:00
=== Performance result === Accuracy: 88.33974895397492 (+/-) 9.798354482493014 Testing Loss: 0.35377315341428733 (+/-) 0.11316917112280157 Precision: 0.8836314964942191 Recall: 0.883397489539749 F1 score: 0.8833774382750127 Testing Time: 0.0021852279806735624 (+/-) 0.0037070249033979204 Training Time: 0.1695471418452562 (+/-) 0.013540274851084373 === Average network evolution === Total hidden node: 4.958158995815899 (+/-) 0.22013299477767206 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:00:45 ETA: 00:00:00
=== Performance result === Accuracy: 89.55732217573224 (+/-) 6.895491428047614 Testing Loss: 0.3437017963022368 (+/-) 0.10074823058498715 Precision: 0.8956303902354521 Recall: 0.8955732217573221 F1 score: 0.8955684716278671 Testing Time: 0.0025747121627360705 (+/-) 0.0040421790607353585 Training Time: 0.18525898207181668 (+/-) 0.02024829630558933 === Average network evolution === Total hidden node: 3.0418410041841004 (+/-) 0.8068264966154947 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 4 No. of parameters : 30 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:00:48 ETA: 00:00:00
=== Performance result === Accuracy: 89.6794979079498 (+/-) 7.041363011047143 Testing Loss: 0.33780838729946183 (+/-) 0.0870487334977934 Precision: 0.8968148051251472 Recall: 0.8967949790794979 F1 score: 0.896793117075348 Testing Time: 0.0026705574291021753 (+/-) 0.005642939509886746 Training Time: 0.19961824177698112 (+/-) 0.027489794627419302 === Average network evolution === Total hidden node: 3.6317991631799162 (+/-) 1.286554749260399 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=2, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=2, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 2 No. of parameters : 16 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:00:43 ETA: 00:00:00
=== Performance result === Accuracy: 89.3723849372385 (+/-) 6.44937081053829 Testing Loss: 0.34281873678063746 (+/-) 0.0943098900835162 Precision: 0.893882473861106 Recall: 0.8937238493723849 F1 score: 0.8937114928364053 Testing Time: 0.0023850097815860762 (+/-) 0.0037719594898348893 Training Time: 0.17591660790862398 (+/-) 0.015445830944554607 === Average network evolution === Total hidden node: 4.552301255230126 (+/-) 0.5298465646285098 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
100% (240 of 240) |######################| Elapsed Time: 0:00:47 ETA: 00:00:00
=== Performance result === Accuracy: 87.84351464435146 (+/-) 10.712185109631335 Testing Loss: 0.35770354404359683 (+/-) 0.11132133416153382 Precision: 0.8784824356103664 Recall: 0.8784351464435146 F1 score: 0.8784302591488962 Testing Time: 0.0026878751970235274 (+/-) 0.004220639753273804 Training Time: 0.19412217080343716 (+/-) 0.021815606326937558 === Average network evolution === Total hidden node: 5.083682008368201 (+/-) 0.6916306601149412 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 6 No. of parameters : 44 Voting weight: [1.0]
Mean Accuracy: 89.12420168067229 Std Accuracy: 8.00382277318376 Hidden Node mean 4.249579831932773 Hidden Node std: 1.1170469735449151 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
97% (234 of 240) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 81.68319327731092 (+/-) 2.2196910727491335 Testing Loss: 0.6541204913323667 (+/-) 0.0021516251926301322 Precision: 0.8296084739783834 Recall: 0.8168319327731093 F1 score: 0.8150031817190354 Testing Time: 0.002143259809798553 (+/-) 0.003772147479357388 Training Time: 4.232430658420595e-06 (+/-) 6.515744177733074e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 4 No. of parameters : 30 Voting weight: [1.0]
95% (228 of 240) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 79.60420168067228 (+/-) 2.362767745994045 Testing Loss: 0.630295464972488 (+/-) 0.004018039388453913 Precision: 0.8249541986146729 Recall: 0.7960420168067227 F1 score: 0.7913393196457598 Testing Time: 0.0022465902216294233 (+/-) 0.004656646648446849 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 3.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 3 No. of parameters : 23 Voting weight: [1.0]
96% (231 of 240) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 60.17563025210084 (+/-) 2.2385269160862897 Testing Loss: 0.6342612692788869 (+/-) 0.008613439970348672 Precision: 0.7553771519023468 Recall: 0.6017563025210084 F1 score: 0.5317131947386157 Testing Time: 0.0020658639298767605 (+/-) 0.004100768714369769 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 4 No. of parameters : 30 Voting weight: [1.0]
93% (225 of 240) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 59.59159663865546 (+/-) 2.196160891380465 Testing Loss: 0.6770254553866988 (+/-) 0.0028720740568141485 Precision: 0.7264134360739493 Recall: 0.5959159663865546 F1 score: 0.5283703438037782 Testing Time: 0.00219577200272504 (+/-) 0.0037161395336315103 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
98% (237 of 240) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 52.94705882352942 (+/-) 2.355980180303979 Testing Loss: 0.6685473347912315 (+/-) 0.007270724783126856 Precision: 0.7335600646016379 Recall: 0.5294705882352941 F1 score: 0.39871695076751057 Testing Time: 0.0021492733674890853 (+/-) 0.004349772483060783 Training Time: 4.189355032784599e-06 (+/-) 6.449430094977448e-05 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=4, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 4 No. of nodes : 5 No. of parameters : 37 Voting weight: [1.0]
Mean Accuracy: 66.79476793248945 Std Accuracy: 11.833397904186391 Hidden Node mean 4.2 Hidden Node std: 0.7483314773547883 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [3]:
%run ADL_weather.ipynb
Number of input: 8 Number of output: 2 Number of batch: 36 All labeled
100% (36 of 36) |########################| Elapsed Time: 0:00:26 ETA: 00:00:00
=== Performance result === Accuracy: 70.29142857142858 (+/-) 4.376650475849666 Testing Loss: 0.5603172157491957 (+/-) 0.04864075346586718 Precision: 0.6753161967968139 Recall: 0.7029142857142857 F1 score: 0.6660907946580805 Testing Time: 0.0020109380994524275 (+/-) 0.0010536084021678846 Training Time: 0.7386781283787318 (+/-) 0.24422472584832222 === Average network evolution === Total hidden node: 6.428571428571429 (+/-) 0.4948716593053935 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:26 ETA: 00:00:00
=== Performance result === Accuracy: 71.41142857142857 (+/-) 4.45830021603823 Testing Loss: 0.5390180970941271 (+/-) 0.05275780424419407 Precision: 0.6921029670566003 Recall: 0.7141142857142857 F1 score: 0.6744157587491696 Testing Time: 0.003435380118233817 (+/-) 0.007631229583872514 Training Time: 0.7403532028198242 (+/-) 0.17609482484291333 === Average network evolution === Total hidden node: 6.6571428571428575 (+/-) 0.5827450872677469 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:26 ETA: 00:00:00
=== Performance result === Accuracy: 71.17714285714285 (+/-) 4.148327422969801 Testing Loss: 0.5535387413842338 (+/-) 0.058574801819985325 Precision: 0.6882273305205227 Recall: 0.7117714285714286 F1 score: 0.6749798701459082 Testing Time: 0.0020357472555977956 (+/-) 0.00046893483673929846 Training Time: 0.7408656324659075 (+/-) 0.0535308196386972 === Average network evolution === Total hidden node: 6.771428571428571 (+/-) 0.6797358430497326 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 9 No. of parameters : 101 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:25 ETA: 00:00:00
=== Performance result === Accuracy: 71.21142857142856 (+/-) 3.94155672136722 Testing Loss: 0.5432083913258143 (+/-) 0.04873687066182523 Precision: 0.6895148364901516 Recall: 0.7121142857142857 F1 score: 0.6692107662229916 Testing Time: 0.0021268231528145925 (+/-) 0.00048225709923842394 Training Time: 0.7328625406537738 (+/-) 0.18463343284896594 === Average network evolution === Total hidden node: 6.3428571428571425 (+/-) 0.4746642207381757 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 71.48571428571428 (+/-) 4.30900338872942 Testing Loss: 0.5373030432632991 (+/-) 0.04702161515288952 Precision: 0.6951253218095428 Recall: 0.7148571428571429 F1 score: 0.6948906805456576 Testing Time: 0.0019490242004394532 (+/-) 0.0004504565675305782 Training Time: 0.6975211415972028 (+/-) 0.16406499271991057 === Average network evolution === Total hidden node: 7.0285714285714285 (+/-) 0.5062870041905528 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 9 No. of parameters : 101 Voting weight: [1.0]
Mean Accuracy: 71.33647058823529 Std Accuracy: 4.1326489741148595 Hidden Node mean 6.6647058823529415 Hidden Node std: 0.593534950128652 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (36 of 36) |########################| Elapsed Time: 0:00:12 ETA: 00:00:00
=== Performance result === Accuracy: 69.79999999999998 (+/-) 4.225095096140271 Testing Loss: 0.568497302702495 (+/-) 0.04643767822172233 Precision: 0.6703645846943086 Recall: 0.698 F1 score: 0.6262666403970503 Testing Time: 0.0018880162920270648 (+/-) 0.0004995125572197143 Training Time: 0.3426097461155483 (+/-) 0.021421758937124717 === Average network evolution === Total hidden node: 5.914285714285715 (+/-) 0.49979587670102565 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:12 ETA: 00:00:00
=== Performance result === Accuracy: 70.03999999999999 (+/-) 4.432039195288262 Testing Loss: 0.5747504830360413 (+/-) 0.04755894931252556 Precision: 0.6774331658051788 Recall: 0.7004 F1 score: 0.6276555197754108 Testing Time: 0.0018604482923235213 (+/-) 0.0004626668745170628 Training Time: 0.34218998636518205 (+/-) 0.01608545623308812 === Average network evolution === Total hidden node: 6.8 (+/-) 0.39999999999999997 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:12 ETA: 00:00:00
=== Performance result === Accuracy: 70.13714285714283 (+/-) 4.220973869637582 Testing Loss: 0.5655117792742593 (+/-) 0.03979251845321257 Precision: 0.6766805128249144 Recall: 0.7013714285714285 F1 score: 0.6344410167816626 Testing Time: 0.001930373055594308 (+/-) 0.0005812613030092886 Training Time: 0.3663158416748047 (+/-) 0.04233670725271423 === Average network evolution === Total hidden node: 5.885714285714286 (+/-) 0.4642307659791977 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:13 ETA: 00:00:00
=== Performance result === Accuracy: 70.11428571428571 (+/-) 4.490825113949732 Testing Loss: 0.556182290826525 (+/-) 0.046483354730274304 Precision: 0.6728749397121206 Recall: 0.7011428571428572 F1 score: 0.6437564109718324 Testing Time: 0.0030959129333496095 (+/-) 0.007528412511258433 Training Time: 0.37491981642586847 (+/-) 0.036138210556301766 === Average network evolution === Total hidden node: 6.2 (+/-) 0.39999999999999997 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:12 ETA: 00:00:00
=== Performance result === Accuracy: 69.18857142857142 (+/-) 4.662542006157549 Testing Loss: 0.5824429069246565 (+/-) 0.041398074528288795 Precision: 0.6843182318966381 Recall: 0.6918857142857143 F1 score: 0.5850625545872097 Testing Time: 0.0018378325871058873 (+/-) 0.0004865534644811643 Training Time: 0.3477430820465088 (+/-) 0.020621625905158585 === Average network evolution === Total hidden node: 5.114285714285714 (+/-) 0.318157963590287 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
Mean Accuracy: 70.03999999999999 Std Accuracy: 4.3542865965282855 Hidden Node mean 5.9941176470588236 Hidden Node std: 0.6729833388684029 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (36 of 36) |########################| Elapsed Time: 0:00:06 ETA: 00:00:00
=== Performance result === Accuracy: 68.27428571428571 (+/-) 5.377856336451375 Testing Loss: 0.6056071230343409 (+/-) 0.04500709694597638 Precision: 0.6272728651657223 Recall: 0.6827428571428571 F1 score: 0.595136312006994 Testing Time: 0.0015383924756731306 (+/-) 0.0004981429603623633 Training Time: 0.17295222282409667 (+/-) 0.01187024822655671 === Average network evolution === Total hidden node: 7.3428571428571425 (+/-) 0.4746642207381757 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:06 ETA: 00:00:00
=== Performance result === Accuracy: 70.76571428571428 (+/-) 3.923987975599958 Testing Loss: 0.5549135335854122 (+/-) 0.04424477072355443 Precision: 0.689669199362978 Recall: 0.7076571428571429 F1 score: 0.6444427087463735 Testing Time: 0.0015115397317068918 (+/-) 0.0004987801339801137 Training Time: 0.1816798346383231 (+/-) 0.019381657726824937 === Average network evolution === Total hidden node: 8.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:06 ETA: 00:00:00
=== Performance result === Accuracy: 68.59428571428572 (+/-) 5.094643846076715 Testing Loss: 0.592691513470241 (+/-) 0.04850196512822754 Precision: 0.6375194670468896 Recall: 0.6859428571428572 F1 score: 0.5912795001106645 Testing Time: 0.001851585933140346 (+/-) 0.00048575638179954375 Training Time: 0.19108799525669642 (+/-) 0.015204977644463286 === Average network evolution === Total hidden node: 6.742857142857143 (+/-) 0.43705881545081016 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 6 No. of parameters : 68 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:06 ETA: 00:00:00
=== Performance result === Accuracy: 69.16000000000001 (+/-) 5.022645859362516 Testing Loss: 0.5884550264903478 (+/-) 0.03993871637412888 Precision: 0.6786299149186737 Recall: 0.6916 F1 score: 0.5860484288258995 Testing Time: 0.003051280975341797 (+/-) 0.008394083795294457 Training Time: 0.18359429495675222 (+/-) 0.018547829356493586 === Average network evolution === Total hidden node: 5.857142857142857 (+/-) 0.3499271061118826 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 6 No. of parameters : 68 Voting weight: [1.0]
100% (36 of 36) |########################| Elapsed Time: 0:00:06 ETA: 00:00:00
=== Performance result === Accuracy: 68.37142857142857 (+/-) 5.12788296214621 Testing Loss: 0.6110692228589739 (+/-) 0.0441945344667759 Precision: 0.6229808252718423 Recall: 0.6837142857142857 F1 score: 0.5745587746644596 Testing Time: 0.0017094407762799945 (+/-) 0.0006119627351107019 Training Time: 0.18425038201468333 (+/-) 0.025416198429328768 === Average network evolution === Total hidden node: 5.171428571428572 (+/-) 0.37688302737922635 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
Mean Accuracy: 69.19294117647058 Std Accuracy: 5.005168816223159 Hidden Node mean 6.629411764705883 Hidden Node std: 1.0727999133012824 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
5% (2 of 36) |# | Elapsed Time: 0:00:00 ETA: 0:00:11C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 68.60000000000001 (+/-) 5.16458528600996 Testing Loss: 0.6579263087581185 (+/-) 0.009342899854395447 Precision: 0.470596 Recall: 0.686 F1 score: 0.5582396204033215 Testing Time: 0.0008799959631527171 (+/-) 0.0004023326871068336 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 7.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
97% (35 of 36) |####################### | Elapsed Time: 0:00:00 ETA: 0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 68.60000000000001 (+/-) 5.16458528600996 Testing Loss: 0.6028559856555041 (+/-) 0.03574550459060763 Precision: 0.470596 Recall: 0.686 F1 score: 0.5582396204033215 Testing Time: 0.001261318431181066 (+/-) 0.000555834964380562 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 7.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
5% (2 of 36) |# | Elapsed Time: 0:00:00 ETA: 0:00:11C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 68.60000000000001 (+/-) 5.16458528600996 Testing Loss: 0.6260132140973035 (+/-) 0.036060178115815446 Precision: 0.470596 Recall: 0.686 F1 score: 0.5582396204033215 Testing Time: 0.0011140248354743509 (+/-) 0.00046972028819113 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 9.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 9 No. of parameters : 101 Voting weight: [1.0]
19% (7 of 36) |#### | Elapsed Time: 0:00:00 ETA: 0:00:10C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 68.60000000000001 (+/-) 5.16458528600996 Testing Loss: 0.6113338330212761 (+/-) 0.029883802257597958 Precision: 0.470596 Recall: 0.686 F1 score: 0.5582396204033215 Testing Time: 0.0027571986703311697 (+/-) 0.009076499786360336 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
88% (32 of 36) |##################### | Elapsed Time: 0:00:00 ETA: 0:00:01C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 68.60000000000001 (+/-) 5.16458528600996 Testing Loss: 0.6157613940098706 (+/-) 0.04607704347878779 Precision: 0.470596 Recall: 0.686 F1 score: 0.5582396204033215 Testing Time: 0.0011732858770033892 (+/-) 0.0003826204326335119 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
Mean Accuracy: 68.43030303030304 Std Accuracy: 5.148020204834549 Hidden Node mean 6.6 Hidden Node std: 1.4966629547095767 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [4]:
%run ADL_rfid.ipynb
Number of input: 3 Number of output: 4 Number of batch: 560 All labeled
100% (560 of 560) |######################| Elapsed Time: 0:07:07 ETA: 00:00:00
=== Performance result === Accuracy: 98.02754919499105 (+/-) 7.7213010367537 Testing Loss: 0.10583574941701694 (+/-) 0.19977435405127758 Precision: 0.9802631927667363 Recall: 0.9802754919499106 F1 score: 0.98024016923772 Testing Time: 0.004872995966875489 (+/-) 0.005325282590282859 Training Time: 0.7564038125688147 (+/-) 0.06931505400848331 === Average network evolution === Total hidden node: 32.833631484794275 (+/-) 11.311931716368724 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=44, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=44, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 44 No. of parameters : 356 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:07:16 ETA: 00:00:00
=== Performance result === Accuracy: 98.31377459749554 (+/-) 6.542701888855917 Testing Loss: 0.09517400123742084 (+/-) 0.18718034556961077 Precision: 0.9831476479309779 Recall: 0.9831377459749553 F1 score: 0.9831030644810734 Testing Time: 0.005186723681809864 (+/-) 0.005643882638682042 Training Time: 0.7721664031204469 (+/-) 0.06594642737183061 === Average network evolution === Total hidden node: 36.840787119856884 (+/-) 10.642073154523906 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=47, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=47, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 47 No. of parameters : 380 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:06:28 ETA: 00:00:00
=== Performance result === Accuracy: 98.26833631484794 (+/-) 6.980226294927336 Testing Loss: 0.10266793742009533 (+/-) 0.18976685920413258 Precision: 0.9827022056623266 Recall: 0.9826833631484795 F1 score: 0.9826588410628426 Testing Time: 0.0046912775056733216 (+/-) 0.0038253311855971253 Training Time: 0.6874949053489672 (+/-) 0.04431513562517215 === Average network evolution === Total hidden node: 31.43649373881932 (+/-) 10.184829955884528 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=41, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=41, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 41 No. of parameters : 332 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:06:19 ETA: 00:00:00
=== Performance result === Accuracy: 98.08908765652953 (+/-) 7.070430683076858 Testing Loss: 0.11407390756995713 (+/-) 0.20877548833869164 Precision: 0.9808662049106773 Recall: 0.9808908765652952 F1 score: 0.9808494875699509 Testing Time: 0.0046198086576512976 (+/-) 0.0037262644740469215 Training Time: 0.670405328593655 (+/-) 0.02927101698006069 === Average network evolution === Total hidden node: 30.987477638640428 (+/-) 11.10986864432801 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=42, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=42, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 42 No. of parameters : 340 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:06:21 ETA: 00:00:00
=== Performance result === Accuracy: 98.19570661896243 (+/-) 6.984930072254507 Testing Loss: 0.09648956943300015 (+/-) 0.1924690357670467 Precision: 0.9820246842664342 Recall: 0.9819570661896243 F1 score: 0.9819110920128582 Testing Time: 0.004874389798568698 (+/-) 0.004377899296036244 Training Time: 0.6733644737967012 (+/-) 0.04463943449538615 === Average network evolution === Total hidden node: 34.51520572450805 (+/-) 11.067658305986456 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=45, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=45, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 45 No. of parameters : 364 Voting weight: [1.0]
Mean Accuracy: 98.29025089605734 Std Accuracy: 6.558571912272443 Hidden Node mean 33.37132616487455 Hidden Node std: 11.030867510138673 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (560 of 560) |######################| Elapsed Time: 0:03:07 ETA: 00:00:00
=== Performance result === Accuracy: 97.19141323792486 (+/-) 9.139088118310458 Testing Loss: 0.1602632498371953 (+/-) 0.22863448992052193 Precision: 0.9719292578200124 Recall: 0.9719141323792486 F1 score: 0.971916964760968 Testing Time: 0.004576175072112109 (+/-) 0.004125611984330837 Training Time: 0.3270158076755476 (+/-) 0.01677785599647808 === Average network evolution === Total hidden node: 30.608228980322004 (+/-) 8.436991781202778 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=40, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=40, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 40 No. of parameters : 324 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:03:03 ETA: 00:00:00
=== Performance result === Accuracy: 97.28443649373882 (+/-) 9.110863063168381 Testing Loss: 0.1588941642944576 (+/-) 0.2391616034399491 Precision: 0.9728152598324031 Recall: 0.9728443649373882 F1 score: 0.9727742203518411 Testing Time: 0.004511605008556932 (+/-) 0.003586433155762893 Training Time: 0.319773612167413 (+/-) 0.01299459331266135 === Average network evolution === Total hidden node: 31.069767441860463 (+/-) 9.573954593838387 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=41, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=41, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 41 No. of parameters : 332 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:03:02 ETA: 00:00:00
=== Performance result === Accuracy: 96.84114490161001 (+/-) 10.784555781853948 Testing Loss: 0.18069382194489614 (+/-) 0.2635218865173473 Precision: 0.9683720646868362 Recall: 0.9684114490161002 F1 score: 0.9683720892766532 Testing Time: 0.0040993131763819935 (+/-) 0.0034184466562314168 Training Time: 0.31974434212836467 (+/-) 0.016316177057959277 === Average network evolution === Total hidden node: 23.887298747763865 (+/-) 8.885668127381456 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=34, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=34, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 34 No. of parameters : 276 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:03:00 ETA: 00:00:00
=== Performance result === Accuracy: 97.16064400715564 (+/-) 9.47928861488502 Testing Loss: 0.16395533588350034 (+/-) 0.24623985928762596 Precision: 0.971592994756987 Recall: 0.9716064400715564 F1 score: 0.9715195558415151 Testing Time: 0.004318036418907971 (+/-) 0.003551642819236627 Training Time: 0.31576817321436135 (+/-) 0.013597795949352978 === Average network evolution === Total hidden node: 26.105545617173526 (+/-) 9.612601003243674 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=37, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=37, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 37 No. of parameters : 300 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:02:59 ETA: 00:00:00
=== Performance result === Accuracy: 97.11878354203935 (+/-) 10.050885984568314 Testing Loss: 0.16422890465913176 (+/-) 0.25091905637381584 Precision: 0.9712158754269345 Recall: 0.9711878354203936 F1 score: 0.9711154125256082 Testing Time: 0.004337646880175432 (+/-) 0.0038182851119739343 Training Time: 0.31359051506506525 (+/-) 0.0138674977672068 === Average network evolution === Total hidden node: 27.543828264758496 (+/-) 9.437552611661422 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=38, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=38, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 38 No. of parameters : 308 Voting weight: [1.0]
N/A% (0 of 560) | | Elapsed Time: 0:00:00 ETA: --:--:--
Mean Accuracy: 97.24301075268818 Std Accuracy: 9.28754279400219 Hidden Node mean 27.877777777777776 Hidden Node std: 9.565007203534343 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (560 of 560) |######################| Elapsed Time: 0:01:33 ETA: 00:00:00
=== Performance result === Accuracy: 95.01860465116278 (+/-) 13.261274712392677 Testing Loss: 0.29812649273595143 (+/-) 0.30080228160306777 Precision: 0.9504975087505442 Recall: 0.9501860465116279 F1 score: 0.9501623006284221 Testing Time: 0.003789654358128529 (+/-) 0.0037115292778900073 Training Time: 0.1604721840464365 (+/-) 0.010140744547632015 === Average network evolution === Total hidden node: 17.547406082289804 (+/-) 6.7230537798450865 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=27, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=27, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 27 No. of parameters : 220 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:01:33 ETA: 00:00:00
=== Performance result === Accuracy: 95.88050089445437 (+/-) 11.304858095741277 Testing Loss: 0.2592303505590542 (+/-) 0.2845174237879515 Precision: 0.9587814439468564 Recall: 0.9588050089445438 F1 score: 0.9587316285435442 Testing Time: 0.0038978149298052028 (+/-) 0.003781978613684488 Training Time: 0.15987096873506876 (+/-) 0.009208457808173295 === Average network evolution === Total hidden node: 19.935599284436492 (+/-) 7.300070033511161 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=29, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=29, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 29 No. of parameters : 236 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:01:34 ETA: 00:00:00
=== Performance result === Accuracy: 94.84579606440072 (+/-) 13.43551667949519 Testing Loss: 0.29246724934745344 (+/-) 0.3028206608801674 Precision: 0.9487262011808772 Recall: 0.9484579606440071 F1 score: 0.948393189447999 Testing Time: 0.0038992740173885774 (+/-) 0.003850099395327466 Training Time: 0.16123658983779934 (+/-) 0.009224956735775666 === Average network evolution === Total hidden node: 19.561717352415027 (+/-) 6.6960085768371265 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=29, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=29, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 29 No. of parameters : 236 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:01:38 ETA: 00:00:00
=== Performance result === Accuracy: 94.73989266547404 (+/-) 13.575447451654385 Testing Loss: 0.28928095361547734 (+/-) 0.30191621277125724 Precision: 0.947470467436047 Recall: 0.9473989266547406 F1 score: 0.9472710209843055 Testing Time: 0.0038550049333111757 (+/-) 0.0035360793383130665 Training Time: 0.16794684747890412 (+/-) 0.018580007218041907 === Average network evolution === Total hidden node: 18.220035778175312 (+/-) 6.878700578199054 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=27, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=27, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 27 No. of parameters : 220 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:01:42 ETA: 00:00:00
=== Performance result === Accuracy: 95.17459749552772 (+/-) 13.031784405913566 Testing Loss: 0.2735860873286967 (+/-) 0.2974053074192794 Precision: 0.951738021266323 Recall: 0.9517459749552772 F1 score: 0.9516377567739979 Testing Time: 0.004041421392096177 (+/-) 0.0036897660872174027 Training Time: 0.17515976228526325 (+/-) 0.020322992200329956 === Average network evolution === Total hidden node: 20.125223613595708 (+/-) 7.067681280930563 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=29, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=29, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 29 No. of parameters : 236 Voting weight: [1.0]
Mean Accuracy: 95.2426523297491 Std Accuracy: 12.691178382473245 Hidden Node mean 19.099283154121864 Hidden Node std: 6.998655748201287 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
100% (560 of 560) |######################| Elapsed Time: 0:00:03 ETA: 00:00:00
=== Performance result === Accuracy: 28.906810035842295 (+/-) 2.054017010999749 Testing Loss: 1.3807804561003134 (+/-) 0.011864229444396562
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.26573461673729293 Recall: 0.28906810035842295 F1 score: 0.16936229501124764 Testing Time: 0.0029610976523395936 (+/-) 0.0038498457408812675 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 9.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 9 No. of parameters : 76 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:00:03 ETA: 00:00:00
=== Performance result === Accuracy: 25.01541218637993 (+/-) 0.14899577341875636 Testing Loss: 1.3766053763341732 (+/-) 0.002995447889610214
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.06257708468544854 Recall: 0.25015412186379926 F1 score: 0.10011099206257089 Testing Time: 0.0027945870566966286 (+/-) 0.0038950997191526 Training Time: 1.7877120698224687e-06 (+/-) 4.2191519830838466e-05 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 5 No. of parameters : 44 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:00:03 ETA: 00:00:00
=== Performance result === Accuracy: 41.80465949820788 (+/-) 5.036004397957265 Testing Loss: 1.3028935386288552 (+/-) 0.011201653772525988
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.5534162706599883 Recall: 0.41804659498207886 F1 score: 0.34071141256068216 Testing Time: 0.0026846047370664536 (+/-) 0.003532780724412595 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 8.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 8 No. of parameters : 68 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:00:03 ETA: 00:00:00
=== Performance result === Accuracy: 41.11756272401434 (+/-) 3.671738399844149 Testing Loss: 1.3661051234464063 (+/-) 0.004718206055022647
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.21905520159042383 Recall: 0.4111756272401434 F1 score: 0.2835440709126196 Testing Time: 0.002659626759081331 (+/-) 0.003908108753083357 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 6.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 6 No. of parameters : 52 Voting weight: [1.0]
100% (560 of 560) |######################| Elapsed Time: 0:00:03 ETA: 00:00:00
=== Performance result === Accuracy: 25.010035842293906 (+/-) 0.11565459082289971 Testing Loss: 1.3951907897081 (+/-) 0.004390485498581702
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.0625501892832826 Recall: 0.2501003584229391 F1 score: 0.10007226837722473 Testing Time: 0.0029908222109613454 (+/-) 0.0037435923977363725 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 8.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=3, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=4, bias=True) ) No. of inputs : 3 No. of nodes : 8 No. of parameters : 68 Voting weight: [1.0]
Mean Accuracy: 32.36840215439857 Std Accuracy: 8.109569659857073 Hidden Node mean 7.2 Hidden Node std: 1.469693845669907 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [5]:
%run ADL_pmnist.ipynb
Number of input: 784 Number of output: 10 Number of batch: 138 All labeled
100% (138 of 138) |######################| Elapsed Time: 0:01:36 ETA: 00:00:00
=== Performance result === Accuracy: 84.21605839416058 (+/-) 12.653635313755666 Testing Loss: 0.5299952784757109 (+/-) 0.3892834901103308 Precision: 0.8416550147546307 Recall: 0.8421605839416059 F1 score: 0.8416725795317286 Testing Time: 0.003857189721434656 (+/-) 0.004000561553988171 Training Time: 0.6990314017247109 (+/-) 0.033090851766374926 === Average network evolution === Total hidden node: 17.56934306569343 (+/-) 2.4518594773976075 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:34 ETA: 00:00:00
=== Performance result === Accuracy: 84.74598540145986 (+/-) 12.917492325160863 Testing Loss: 0.5100546096907045 (+/-) 0.40021389069832347 Precision: 0.8472381593398333 Recall: 0.8474598540145986 F1 score: 0.8471865724310298 Testing Time: 0.004004248737418738 (+/-) 0.004329616346014479 Training Time: 0.6855113732553747 (+/-) 0.019572027733594493 === Average network evolution === Total hidden node: 22.014598540145986 (+/-) 4.60241986724923 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=30, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=30, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 30 No. of parameters : 23860 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:35 ETA: 00:00:00
=== Performance result === Accuracy: 84.58540145985403 (+/-) 12.4274862550659 Testing Loss: 0.5213972165038551 (+/-) 0.3966300492360766 Precision: 0.8453992297654769 Recall: 0.8458540145985401 F1 score: 0.8454436146001473 Testing Time: 0.004104240097268654 (+/-) 0.003651723519130708 Training Time: 0.6909720723646401 (+/-) 0.028694711395282294 === Average network evolution === Total hidden node: 23.197080291970803 (+/-) 6.9511742599429445 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=36, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=36, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 36 No. of parameters : 28630 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:36 ETA: 00:00:00
=== Performance result === Accuracy: 84.25255474452555 (+/-) 12.124446441074227 Testing Loss: 0.5319883200156428 (+/-) 0.3924745387530294 Precision: 0.8419088124333297 Recall: 0.8425255474452554 F1 score: 0.8420690044013521 Testing Time: 0.003943894031274058 (+/-) 0.0035111099992132188 Training Time: 0.6999243854606239 (+/-) 0.03308558338599792 === Average network evolution === Total hidden node: 18.204379562043794 (+/-) 2.546326067076693 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=21, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=21, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 21 No. of parameters : 16705 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:33 ETA: 00:00:00
=== Performance result === Accuracy: 84.44671532846716 (+/-) 12.7199656331015 Testing Loss: 0.5224682564174172 (+/-) 0.381670897769678 Precision: 0.8441187917103862 Recall: 0.8444671532846715 F1 score: 0.844042617245612 Testing Time: 0.0038340770415145986 (+/-) 0.0035691532330264994 Training Time: 0.6770647616281996 (+/-) 0.022852488245372768 === Average network evolution === Total hidden node: 17.605839416058394 (+/-) 1.9756091705220327 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
Mean Accuracy: 84.70382352941178 Std Accuracy: 12.255283977967334 Hidden Node mean 19.764705882352942 Hidden Node std: 4.774988903771596 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (138 of 138) |######################| Elapsed Time: 0:00:46 ETA: 00:00:00
=== Performance result === Accuracy: 80.88613138686131 (+/-) 14.290965869100262 Testing Loss: 0.6502768283669096 (+/-) 0.433513438047007 Precision: 0.8072198139913351 Recall: 0.8088613138686132 F1 score: 0.8071815540058104 Testing Time: 0.003489384685989714 (+/-) 0.0037670206135806553 Training Time: 0.33638614285601315 (+/-) 0.016971683304853734 === Average network evolution === Total hidden node: 15.211678832116789 (+/-) 1.9005594909402377 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=17, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=17, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 17 No. of parameters : 13525 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:46 ETA: 00:00:00
=== Performance result === Accuracy: 81.83795620437955 (+/-) 14.012310184220583 Testing Loss: 0.617952929959245 (+/-) 0.410199837927235 Precision: 0.8173447148396881 Recall: 0.8183795620437956 F1 score: 0.8173417712267829 Testing Time: 0.003627864113689339 (+/-) 0.003503028857547654 Training Time: 0.33586348582358255 (+/-) 0.012826728466561556 === Average network evolution === Total hidden node: 18.474452554744527 (+/-) 1.9145249338155677 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:46 ETA: 00:00:00
=== Performance result === Accuracy: 81.88175182481751 (+/-) 13.50954363381249 Testing Loss: 0.6170137628477855 (+/-) 0.4044168899260186 Precision: 0.81778940535475 Recall: 0.8188175182481752 F1 score: 0.817702378637757 Testing Time: 0.003910365765982301 (+/-) 0.004675826452204106 Training Time: 0.33516328526239325 (+/-) 0.010340495500602464 === Average network evolution === Total hidden node: 18.1021897810219 (+/-) 2.411301467445096 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:47 ETA: 00:00:00
=== Performance result === Accuracy: 81.10218978102189 (+/-) 14.250339820014153 Testing Loss: 0.6383997452628873 (+/-) 0.41971078258978967 Precision: 0.8097897103781534 Recall: 0.8110218978102189 F1 score: 0.8100443431849681 Testing Time: 0.0035352063004987955 (+/-) 0.003258019551746477 Training Time: 0.3381819672828173 (+/-) 0.014580208957045619 === Average network evolution === Total hidden node: 16.583941605839417 (+/-) 2.2201543175352505 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=19, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=19, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 19 No. of parameters : 15115 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:46 ETA: 00:00:00
=== Performance result === Accuracy: 81.20729927007298 (+/-) 14.175158877501453 Testing Loss: 0.6442732809886446 (+/-) 0.4113639593405351 Precision: 0.8110618715073876 Recall: 0.81207299270073 F1 score: 0.8109622671410924 Testing Time: 0.0035519443289206845 (+/-) 0.003577710137397939 Training Time: 0.3305896024634368 (+/-) 0.009684010989262899 === Average network evolution === Total hidden node: 15.138686131386862 (+/-) 1.500279690478304 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=16, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=16, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 16 No. of parameters : 12730 Voting weight: [1.0]
N/A% (0 of 138) | | Elapsed Time: 0:00:00 ETA: --:--:--
Mean Accuracy: 81.73029411764706 Std Accuracy: 13.494134670356974 Hidden Node mean 16.726470588235294 Hidden Node std: 2.442612972103417 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (138 of 138) |######################| Elapsed Time: 0:00:23 ETA: 00:00:00
=== Performance result === Accuracy: 74.95182481751824 (+/-) 17.048244562283898 Testing Loss: 0.8686235523136863 (+/-) 0.48007725273905405 Precision: 0.746902668866979 Recall: 0.7495182481751825 F1 score: 0.7462196258098396 Testing Time: 0.00334940165498831 (+/-) 0.004532571562757123 Training Time: 0.169146341128941 (+/-) 0.009097344356041301 === Average network evolution === Total hidden node: 13.510948905109489 (+/-) 1.3019737308907036 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=15, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=15, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 15 No. of parameters : 11935 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 78.33284671532847 (+/-) 15.913860778859181 Testing Loss: 0.7683567316645253 (+/-) 0.4606781109564946 Precision: 0.7823958145795648 Recall: 0.7833284671532846 F1 score: 0.7818418313450543 Testing Time: 0.0034434882393718637 (+/-) 0.0033551168808069287 Training Time: 0.17254897799805133 (+/-) 0.012978697164451361 === Average network evolution === Total hidden node: 18.072992700729927 (+/-) 1.3270627051139614 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 77.16934306569344 (+/-) 15.722908861660649 Testing Loss: 0.7864098247602909 (+/-) 0.448316591996406 Precision: 0.7692460019539521 Recall: 0.7716934306569343 F1 score: 0.7691168634308589 Testing Time: 0.0037644153093769604 (+/-) 0.00496851830327111 Training Time: 0.17065287854549657 (+/-) 0.008547987984671731 === Average network evolution === Total hidden node: 16.956204379562045 (+/-) 1.0454532292643988 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=18, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=18, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 18 No. of parameters : 14320 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 75.32262773722628 (+/-) 17.106591518679807 Testing Loss: 0.8652569673357219 (+/-) 0.48437066011097796 Precision: 0.7507739176934152 Recall: 0.7532262773722628 F1 score: 0.7507158147571324 Testing Time: 0.0033046851192947723 (+/-) 0.0031900432097960174 Training Time: 0.17169494350461195 (+/-) 0.011608721505436966 === Average network evolution === Total hidden node: 12.985401459854014 (+/-) 0.7544070176761591 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=14, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=14, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 14 No. of parameters : 11140 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 73.54014598540147 (+/-) 18.02475775009171 Testing Loss: 0.9113261250466326 (+/-) 0.50209809040161 Precision: 0.7327828379796012 Recall: 0.7354014598540146 F1 score: 0.7321587060237189 Testing Time: 0.0032818717678097914 (+/-) 0.00318632995556695 Training Time: 0.17031842078605708 (+/-) 0.009504997955329752 === Average network evolution === Total hidden node: 12.167883211678832 (+/-) 1.3211880467872947 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=14, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=14, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 14 No. of parameters : 11140 Voting weight: [1.0]
Mean Accuracy: 76.22882352941176 Std Accuracy: 16.379825964152275 Hidden Node mean 14.745588235294118 Hidden Node std: 2.611903397880862 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
91% (126 of 138) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 25.333823529411763 (+/-) 25.502007477463867 Testing Loss: 2.064229090424145 (+/-) 0.4104954158584063 Precision: 0.4374358587116846 Recall: 0.25333823529411764 F1 score: 0.2646958627693986 Testing Time: 0.0027500513721914854 (+/-) 0.004273491811467424 Training Time: 7.338383618523093e-06 (+/-) 8.526431262786378e-05 === Average network evolution === Total hidden node: 14.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=14, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=14, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 14 No. of parameters : 11140 Voting weight: [1.0]
91% (126 of 138) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 22.43235294117647 (+/-) 23.74488453377888 Testing Loss: 2.1428030466332153 (+/-) 0.37636946841250846 Precision: 0.47727134531972476 Recall: 0.2243235294117647 F1 score: 0.23373218858229994 Testing Time: 0.002472253406749052 (+/-) 0.003273756324019873 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 12.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 12 No. of parameters : 9550 Voting weight: [1.0]
91% (126 of 138) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 19.908823529411762 (+/-) 21.856831852213094 Testing Loss: 2.1821167635567047 (+/-) 0.38966945128539 Precision: 0.5288993945686016 Recall: 0.19908823529411765 F1 score: 0.19795959330194587 Testing Time: 0.002552656566395479 (+/-) 0.002942262730630038 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 13.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 13 No. of parameters : 10345 Voting weight: [1.0]
99% (137 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 22.325000000000003 (+/-) 24.809852614731632 Testing Loss: 2.1229126427103493 (+/-) 0.3791066752811218 Precision: 0.519875513686255 Recall: 0.22325 F1 score: 0.23257330587375025 Testing Time: 0.002714463893105002 (+/-) 0.003269130798608319 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 13.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 13 No. of parameters : 10345 Voting weight: [1.0]
96% (133 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 23.805882352941175 (+/-) 25.919308509935558 Testing Loss: 2.127225234228022 (+/-) 0.41781618269887444 Precision: 0.6289586207790309 Recall: 0.23805882352941177 F1 score: 0.2664406448968325 Testing Time: 0.0027649385087630328 (+/-) 0.0035959664330713543 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 15.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=15, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=15, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 15 No. of parameters : 11935 Voting weight: [1.0]
Mean Accuracy: 22.64474074074074 Std Accuracy: 24.526693812604037 Hidden Node mean 13.4 Hidden Node std: 1.019803902718557 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [6]:
%run ADL_rmnist.ipynb
Number of input: 784 Number of output: 10 Number of batch: 138 All labeled
100% (138 of 138) |######################| Elapsed Time: 0:01:37 ETA: 00:00:00
=== Performance result === Accuracy: 89.20875912408759 (+/-) 5.263390631710589 Testing Loss: 0.38442177736084826 (+/-) 0.20877860168352383 Precision: 0.8917050037765241 Recall: 0.8920875912408759 F1 score: 0.8917881746379951 Testing Time: 0.0037725180605032147 (+/-) 0.0029109260512575064 Training Time: 0.7038712605942775 (+/-) 0.03201929995692592 === Average network evolution === Total hidden node: 18.693430656934307 (+/-) 2.832455394010672 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=23, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=23, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 23 No. of parameters : 18295 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:34 ETA: 00:00:00
=== Performance result === Accuracy: 88.97518248175183 (+/-) 6.084007767429762 Testing Loss: 0.39315680852227836 (+/-) 0.22934648708743813 Precision: 0.8893695798676612 Recall: 0.8897518248175182 F1 score: 0.8893514530366471 Testing Time: 0.003854205138491888 (+/-) 0.003653415689521969 Training Time: 0.6866993782294057 (+/-) 0.01641039802917066 === Average network evolution === Total hidden node: 18.78102189781022 (+/-) 3.1454180056357246 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=24, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=24, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 24 No. of parameters : 19090 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:34 ETA: 00:00:00
=== Performance result === Accuracy: 89.02773722627737 (+/-) 5.80707760877256 Testing Loss: 0.3852812723413001 (+/-) 0.19998946979281998 Precision: 0.8897281753500507 Recall: 0.8902773722627737 F1 score: 0.889828165551493 Testing Time: 0.003944682378838532 (+/-) 0.0038287393661166147 Training Time: 0.6830965880929989 (+/-) 0.02386645554321748 === Average network evolution === Total hidden node: 20.233576642335766 (+/-) 3.989516871986278 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=28, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=28, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 28 No. of parameters : 22270 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:33 ETA: 00:00:00
=== Performance result === Accuracy: 89.48905109489051 (+/-) 5.017744845361881 Testing Loss: 0.37946582340846097 (+/-) 0.21104281442560785 Precision: 0.894456103077244 Recall: 0.8948905109489051 F1 score: 0.8945418150721977 Testing Time: 0.003903547342676316 (+/-) 0.0042483712992618106 Training Time: 0.6733669608178801 (+/-) 0.017356437949479744 === Average network evolution === Total hidden node: 20.72992700729927 (+/-) 4.318528489640881 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=28, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=28, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 28 No. of parameters : 22270 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:01:42 ETA: 00:00:00
=== Performance result === Accuracy: 89.83357664233577 (+/-) 5.785588405083019 Testing Loss: 0.36382242066472986 (+/-) 0.22140838424531714 Precision: 0.8979836238111282 Recall: 0.8983357664233577 F1 score: 0.8980156337447938 Testing Time: 0.004454988632759039 (+/-) 0.00500682282650324 Training Time: 0.7452280416975926 (+/-) 0.07224029757590479 === Average network evolution === Total hidden node: 26.094890510948904 (+/-) 7.721316792957612 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=37, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=37, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 37 No. of parameters : 29425 Voting weight: [1.0]
Mean Accuracy: 89.625 Std Accuracy: 4.2059088337298824 Hidden Node mean 20.96323529411765 Hidden Node std: 5.436813632454992 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (138 of 138) |######################| Elapsed Time: 0:00:49 ETA: 00:00:00
=== Performance result === Accuracy: 86.60875912408758 (+/-) 8.016401748937795 Testing Loss: 0.49998656692948656 (+/-) 0.2970231421724114 Precision: 0.8657618305244025 Recall: 0.8660875912408759 F1 score: 0.86545540083863 Testing Time: 0.0037748413364382554 (+/-) 0.004041462608065304 Training Time: 0.35309866571078335 (+/-) 0.024523129108556117 === Average network evolution === Total hidden node: 15.540145985401459 (+/-) 2.106696305060119 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=18, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=18, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 18 No. of parameters : 14320 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:51 ETA: 00:00:00
=== Performance result === Accuracy: 86.8029197080292 (+/-) 7.661868248145967 Testing Loss: 0.4811633670308294 (+/-) 0.2872784448016916 Precision: 0.8677312527129786 Recall: 0.868029197080292 F1 score: 0.8674457213423348 Testing Time: 0.004164881949877217 (+/-) 0.004627601071256578 Training Time: 0.372638042825852 (+/-) 0.03479158438549361 === Average network evolution === Total hidden node: 18.21897810218978 (+/-) 2.993216975234898 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=22, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=22, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 22 No. of parameters : 17500 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:52 ETA: 00:00:00
=== Performance result === Accuracy: 86.66715328467154 (+/-) 7.434866660618423 Testing Loss: 0.4840749332504551 (+/-) 0.2797381019945645 Precision: 0.8660293086155019 Recall: 0.8666715328467153 F1 score: 0.8659433644128327 Testing Time: 0.0040364369858790486 (+/-) 0.004661190528711577 Training Time: 0.37548991189385855 (+/-) 0.03650414882442444 === Average network evolution === Total hidden node: 16.802919708029197 (+/-) 1.8076860074643484 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=19, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=19, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 19 No. of parameters : 15115 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:51 ETA: 00:00:00
=== Performance result === Accuracy: 87.61605839416058 (+/-) 6.081397188394578 Testing Loss: 0.4593784056440757 (+/-) 0.2609606645720423 Precision: 0.8756213963317653 Recall: 0.8761605839416058 F1 score: 0.8756129641498354 Testing Time: 0.0039273004462249085 (+/-) 0.004855012372046954 Training Time: 0.371994711186764 (+/-) 0.03443739223074941 === Average network evolution === Total hidden node: 19.233576642335766 (+/-) 2.572348109970825 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=24, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=24, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 24 No. of parameters : 19090 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:48 ETA: 00:00:00
=== Performance result === Accuracy: 86.52846715328467 (+/-) 7.175698535696609 Testing Loss: 0.49342357345523624 (+/-) 0.2879587840061541 Precision: 0.8645745908058169 Recall: 0.8652846715328467 F1 score: 0.8644812637844438 Testing Time: 0.0035975518887930544 (+/-) 0.0031893891031047543 Training Time: 0.3496038008780375 (+/-) 0.018651515103250214 === Average network evolution === Total hidden node: 16.613138686131386 (+/-) 2.3154453629790837 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=20, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=20, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 20 No. of parameters : 15910 Voting weight: [1.0]
Mean Accuracy: 87.21794117647059 Std Accuracy: 5.86720460516699 Hidden Node mean 17.30735294117647 Hidden Node std: 2.7129220287829976 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (138 of 138) |######################| Elapsed Time: 0:00:25 ETA: 00:00:00
=== Performance result === Accuracy: 83.65401459854016 (+/-) 10.068393957116463 Testing Loss: 0.623548363247057 (+/-) 0.37014799204740256 Precision: 0.8351284807501452 Recall: 0.8365401459854015 F1 score: 0.8351441654546017 Testing Time: 0.00333879289835909 (+/-) 0.00328189061858016 Training Time: 0.1776188220420893 (+/-) 0.015539172806005008 === Average network evolution === Total hidden node: 14.481751824817518 (+/-) 1.4802948501965978 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=17, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=17, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 17 No. of parameters : 13525 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 84.23503649635036 (+/-) 9.172281245515926 Testing Loss: 0.6137293473864994 (+/-) 0.3403226259591167 Precision: 0.8417307021005614 Recall: 0.8423503649635037 F1 score: 0.8408539939216325 Testing Time: 0.003154963472463789 (+/-) 0.00330497857154288 Training Time: 0.17120972515022667 (+/-) 0.00904131677153224 === Average network evolution === Total hidden node: 14.81021897810219 (+/-) 1.645754754260998 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=17, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=17, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 17 No. of parameters : 13525 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 84.43065693430657 (+/-) 9.32986384801257 Testing Loss: 0.5893517212711111 (+/-) 0.3460455462884988 Precision: 0.843721267303986 Recall: 0.8443065693430657 F1 score: 0.8434313176093274 Testing Time: 0.0031467510835967794 (+/-) 0.0034525257244636203 Training Time: 0.17096437502951517 (+/-) 0.008614652568017281 === Average network evolution === Total hidden node: 16.78832116788321 (+/-) 1.568069420360645 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=19, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=19, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 19 No. of parameters : 15115 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 84.46861313868612 (+/-) 9.81824424092342 Testing Loss: 0.5969956469361799 (+/-) 0.3436997668214151 Precision: 0.8443987247139246 Recall: 0.8446861313868613 F1 score: 0.8438607695163073 Testing Time: 0.003366546909304431 (+/-) 0.0041205711683533425 Training Time: 0.1728711824347503 (+/-) 0.010298479654915682 === Average network evolution === Total hidden node: 17.59124087591241 (+/-) 1.9873862985446562 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=21, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=21, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 21 No. of parameters : 16705 Voting weight: [1.0]
100% (138 of 138) |######################| Elapsed Time: 0:00:24 ETA: 00:00:00
=== Performance result === Accuracy: 83.71970802919707 (+/-) 10.009002075256568 Testing Loss: 0.6140992701814992 (+/-) 0.35692050239663226 Precision: 0.8361368785000315 Recall: 0.8371970802919708 F1 score: 0.8360610360382664 Testing Time: 0.0031542464764448847 (+/-) 0.0035443633346721125 Training Time: 0.17139535750785884 (+/-) 0.008651194137348928 === Average network evolution === Total hidden node: 15.875912408759124 (+/-) 1.6453662227377572 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=18, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=18, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 18 No. of parameters : 14320 Voting weight: [1.0]
Mean Accuracy: 84.52029411764707 Std Accuracy: 8.40162798835269 Hidden Node mean 15.923529411764706 Hidden Node std: 2.040769581415076 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
91% (126 of 138) |#################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 52.87352941176471 (+/-) 4.982074268198019 Testing Loss: 1.70448217847768 (+/-) 0.06028631635758112 Precision: 0.6194206399526673 Recall: 0.5287352941176471 F1 score: 0.4966379150300731 Testing Time: 0.002354104729259715 (+/-) 0.004393785955882732 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 12.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 12 No. of parameters : 9550 Voting weight: [1.0]
99% (137 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 60.35588235294117 (+/-) 5.100616524238578 Testing Loss: 1.5087173546061796 (+/-) 0.07931457074769332 Precision: 0.6406761426868426 Recall: 0.6035588235294118 F1 score: 0.5719097962441663 Testing Time: 0.0023840245078591738 (+/-) 0.002936071570207057 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 13.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 13 No. of parameters : 10345 Voting weight: [1.0]
97% (135 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 62.89852941176471 (+/-) 6.396506230358671 Testing Loss: 1.5400805245427525 (+/-) 0.07778116809315058 Precision: 0.6884096134990224 Recall: 0.628985294117647 F1 score: 0.6289849014294346 Testing Time: 0.0022737874704248763 (+/-) 0.002949484099497785 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 13.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 13 No. of parameters : 10345 Voting weight: [1.0]
95% (132 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 56.56029411764706 (+/-) 4.441834534417268 Testing Loss: 1.600654026164728 (+/-) 0.060454918549738036 Precision: 0.6682785836902212 Recall: 0.5656029411764706 F1 score: 0.5422914345961902 Testing Time: 0.0027360442806692686 (+/-) 0.004016387755502349 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 11.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=11, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=11, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 11 No. of parameters : 8755 Voting weight: [1.0]
95% (132 of 138) |##################### | Elapsed Time: 0:00:01 ETA: 0:00:00
=== Performance result === Accuracy: 63.74558823529413 (+/-) 5.6033848442528695 Testing Loss: 1.4753911109531628 (+/-) 0.0806018255153356 Precision: 0.6981774536065212 Recall: 0.6374558823529411 F1 score: 0.6289148080096935 Testing Time: 0.0022810329409206614 (+/-) 0.0030429463503291814 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 13.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=784, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=10, bias=True) ) No. of inputs : 784 No. of nodes : 13 No. of parameters : 10345 Voting weight: [1.0]
Mean Accuracy: 59.255111111111106 Std Accuracy: 6.705914456012545 Hidden Node mean 12.4 Hidden Node std: 0.8 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [7]:
%run ADL_hepmass.ipynb
Number of input: 28 Number of output: 2 Number of batch: 4000 All labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:49:06 ETA: 00:00:00
=== Performance result === Accuracy: 84.27471867966993 (+/-) 1.9651778534340254 Testing Loss: 0.330267546474412 (+/-) 0.0298085893511028 Precision: 0.8444971813332426 Recall: 0.8427471867966991 F1 score: 0.8425459401381118 Testing Time: 0.008208171461009479 (+/-) 0.005566143633784638 Training Time: 0.7113397854153709 (+/-) 0.07011717912570137 === Average network evolution === Total hidden node: 3.8547136784196048 (+/-) 0.5520175202166179 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 3 No. of parameters : 95 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:48:01 ETA: 00:00:00
=== Performance result === Accuracy: 84.34248562140534 (+/-) 1.96683502949259 Testing Loss: 0.329345764480969 (+/-) 0.029565521804856314 Precision: 0.8449849427140919 Recall: 0.8434248562140535 F1 score: 0.843246431985762 Testing Time: 0.009227581756297992 (+/-) 0.006537240098537969 Training Time: 0.6939987721935634 (+/-) 0.059228153575059346 === Average network evolution === Total hidden node: 6.903725931482871 (+/-) 0.3301679476018715 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 7 No. of parameters : 219 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:51:36 ETA: 00:00:00
=== Performance result === Accuracy: 84.38789697424355 (+/-) 2.1638401291256364 Testing Loss: 0.32911720091281876 (+/-) 0.031897747793727456 Precision: 0.8453157754901114 Recall: 0.8438789697424356 F1 score: 0.843715246793188 Testing Time: 0.009940651304336095 (+/-) 0.006515943899486795 Training Time: 0.7462876080930099 (+/-) 0.05905850252163256 === Average network evolution === Total hidden node: 7.434858714678669 (+/-) 0.5193867676857585 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 7 No. of parameters : 219 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:49:24 ETA: 00:00:00
=== Performance result === Accuracy: 84.25331332833208 (+/-) 1.8797451316707565 Testing Loss: 0.33065941628738477 (+/-) 0.029332341065045873 Precision: 0.8442250768667071 Recall: 0.8425331332833208 F1 score: 0.8423381302472389 Testing Time: 0.008174278581699869 (+/-) 0.005233628019318262 Training Time: 0.7157893950535554 (+/-) 0.029865859339877104 === Average network evolution === Total hidden node: 3.4466116529132282 (+/-) 0.5460425236038302 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 3 No. of parameters : 95 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:49:37 ETA: 00:00:00
=== Performance result === Accuracy: 84.35663915978995 (+/-) 2.054844496319103 Testing Loss: 0.3273094044041115 (+/-) 0.03136871028044463 Precision: 0.8451452268971658 Recall: 0.8435663915978995 F1 score: 0.8433860750777953 Testing Time: 0.00952579510691882 (+/-) 0.006281172256604657 Training Time: 0.7174528066025104 (+/-) 0.01771030362514928 === Average network evolution === Total hidden node: 7.910227556889223 (+/-) 0.3379666826781271 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 8 No. of parameters : 250 Voting weight: [1.0]
Mean Accuracy: 84.33150575287644 Std Accuracy: 1.9333762840610735 Hidden Node mean 5.910505252626313 Hidden Node std: 1.9339634955832405 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:25:25 ETA: 00:00:00
=== Performance result === Accuracy: 83.73608402100524 (+/-) 2.2802007801598734 Testing Loss: 0.33798245888526635 (+/-) 0.0355996361254071 Precision: 0.8393397048267965 Recall: 0.8373608402100525 F1 score: 0.8371219459925799 Testing Time: 0.009206158305800358 (+/-) 0.006176437594693677 Training Time: 0.3548685260104012 (+/-) 0.011513308116532955 === Average network evolution === Total hidden node: 4.911477869467367 (+/-) 0.41858045086497236 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 5 No. of parameters : 157 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:25:03 ETA: 00:00:00
=== Performance result === Accuracy: 83.80280070017506 (+/-) 2.491406015490654 Testing Loss: 0.3377445449111282 (+/-) 0.036908027711822125 Precision: 0.8395329339693653 Recall: 0.8380280070017504 F1 score: 0.8378470796771735 Testing Time: 0.00933564195158363 (+/-) 0.006164510772438725 Training Time: 0.3493382015595528 (+/-) 0.009825766971749281 === Average network evolution === Total hidden node: 7.700925231307827 (+/-) 0.5802247920964608 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 8 No. of parameters : 250 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:24:57 ETA: 00:00:00
=== Performance result === Accuracy: 83.79224806201549 (+/-) 2.80505204973652 Testing Loss: 0.33820619355681303 (+/-) 0.03975921309941251 Precision: 0.8394859528110324 Recall: 0.8379224806201551 F1 score: 0.8377343826954374 Testing Time: 0.009186149090640274 (+/-) 0.00626325328826033 Training Time: 0.3481195026649538 (+/-) 0.00944403192134 === Average network evolution === Total hidden node: 5.782945736434108 (+/-) 0.5481554007688418 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 6 No. of parameters : 188 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:25:01 ETA: 00:00:00
=== Performance result === Accuracy: 83.79674918729683 (+/-) 2.210298801080123 Testing Loss: 0.3376355614773063 (+/-) 0.034171457758005105 Precision: 0.8395973502428108 Recall: 0.8379674918729683 F1 score: 0.8377715437863407 Testing Time: 0.009212620409168759 (+/-) 0.006194103125854386 Training Time: 0.3490182176534162 (+/-) 0.010273933757171458 === Average network evolution === Total hidden node: 5.91297824456114 (+/-) 0.39870897844065606 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 6 No. of parameters : 188 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:25:03 ETA: 00:00:00
=== Performance result === Accuracy: 83.99159789947487 (+/-) 2.3756347220140346 Testing Loss: 0.33507401953938304 (+/-) 0.036372755866008466 Precision: 0.8412093828703266 Recall: 0.8399159789947487 F1 score: 0.839762991287483 Testing Time: 0.009374589555172301 (+/-) 0.006435902762300744 Training Time: 0.349297381604007 (+/-) 0.011187877916816044 === Average network evolution === Total hidden node: 6.961740435108777 (+/-) 0.22427471197065021 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 7 No. of parameters : 219 Voting weight: [1.0]
Mean Accuracy: 83.83330665332666 Std Accuracy: 2.369747048114372 Hidden Node mean 6.254627313656829 Hidden Node std: 1.0722743281416995 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:13:23 ETA: 00:00:00
=== Performance result === Accuracy: 83.3966991747937 (+/-) 2.6304446234441183 Testing Loss: 0.3441671575343916 (+/-) 0.04142407049523018 Precision: 0.8357253163748376 Recall: 0.833966991747937 F1 score: 0.8337479100156516 Testing Time: 0.008493842110391794 (+/-) 0.005826768953672994 Training Time: 0.17510843724124162 (+/-) 0.007163980227652877 === Average network evolution === Total hidden node: 4.340585146286571 (+/-) 0.5464479929889785 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 5 No. of parameters : 157 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:13:32 ETA: 00:00:00
=== Performance result === Accuracy: 83.35513878469618 (+/-) 2.313399215110903 Testing Loss: 0.34491376737917506 (+/-) 0.03777271849745239 Precision: 0.8348697876299853 Recall: 0.8335513878469617 F1 score: 0.8333861772989148 Testing Time: 0.009001937321526732 (+/-) 0.006135936037090921 Training Time: 0.17680245615536347 (+/-) 0.008211680116700596 === Average network evolution === Total hidden node: 6.323580895223806 (+/-) 0.4867040174430941 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 6 No. of parameters : 188 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:13:29 ETA: 00:00:00
=== Performance result === Accuracy: 83.3375343835959 (+/-) 2.881361584980513 Testing Loss: 0.3472857065619931 (+/-) 0.047013739008987976 Precision: 0.8353999425426777 Recall: 0.833375343835959 F1 score: 0.8331220002570955 Testing Time: 0.008857515282140847 (+/-) 0.0060651115955736344 Training Time: 0.17625329124238914 (+/-) 0.007162263950789549 === Average network evolution === Total hidden node: 4.516129032258065 (+/-) 0.7048415564932156 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 5 No. of parameters : 157 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:13:27 ETA: 00:00:00
=== Performance result === Accuracy: 83.27941985496375 (+/-) 2.8541707440471957 Testing Loss: 0.351626099661369 (+/-) 0.046221010121858364 Precision: 0.8348148363568347 Recall: 0.8327941985496374 F1 score: 0.8325400236600833 Testing Time: 0.007976404694683345 (+/-) 0.00507851170744298 Training Time: 0.17676449143967052 (+/-) 0.00844617077029112 === Average network evolution === Total hidden node: 3.7881970492623154 (+/-) 0.5375620600669296 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 4 No. of parameters : 126 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:13:25 ETA: 00:00:00
=== Performance result === Accuracy: 83.60165041260315 (+/-) 2.340824030358643 Testing Loss: 0.34333790804809794 (+/-) 0.03818323402283034 Precision: 0.8377873330871999 Recall: 0.8360165041260315 F1 score: 0.835799923710361 Testing Time: 0.00800256462030394 (+/-) 0.00506383917022807 Training Time: 0.17630566081633475 (+/-) 0.007307652848869432 === Average network evolution === Total hidden node: 3.7496874218554637 (+/-) 0.4885375569549067 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 4 No. of parameters : 126 Voting weight: [1.0]
Mean Accuracy: 83.40190095047524 Std Accuracy: 2.5701554890342733 Hidden Node mean 4.543721860930465 Hidden Node std: 1.0929142626345736 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
100% (4000 of 4000) |####################| Elapsed Time: 0:01:43 ETA: 00:00:00
=== Performance result === Accuracy: 51.88724362181091 (+/-) 2.280085908919687 Testing Loss: 0.6889360942293132 (+/-) 0.008801739019871982 Precision: 0.6739113752749148 Recall: 0.5188724362181091 F1 score: 0.3807828713531886 Testing Time: 0.008792701752678402 (+/-) 0.00621113491233278 Training Time: 4.990211780695035e-07 (+/-) 2.2305746833386594e-05 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 5 No. of parameters : 157 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:42 ETA: 00:00:00
=== Performance result === Accuracy: 59.94467233616808 (+/-) 2.2054052431091344 Testing Loss: 0.6553475126557019 (+/-) 0.006080473222214076 Precision: 0.7336454548725878 Recall: 0.5994467233616808 F1 score: 0.5322370776305818 Testing Time: 0.008687302910011371 (+/-) 0.006240351162555132 Training Time: 1.9982911873722505e-06 (+/-) 4.46273008904126e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 4 No. of parameters : 126 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:41 ETA: 00:00:00
=== Performance result === Accuracy: 62.241570785392696 (+/-) 2.1551531445364005 Testing Loss: 0.6735364650415742 (+/-) 0.003394087776875502 Precision: 0.6258176963079225 Recall: 0.622415707853927 F1 score: 0.6198328196016225 Testing Time: 0.008753573077508603 (+/-) 0.00636426305167349 Training Time: 2.494390276803441e-06 (+/-) 4.981293326105004e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 4 No. of parameters : 126 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:43 ETA: 00:00:00
=== Performance result === Accuracy: 50.008404202101055 (+/-) 2.2817827834914235 Testing Loss: 0.7205007633994733 (+/-) 0.013804003489672229
C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
Precision: 0.25008404908407184 Recall: 0.5000840420210105 F1 score: 0.3334267175419617 Testing Time: 0.008766726114083195 (+/-) 0.006287377636701752 Training Time: 1.9518359414692697e-06 (+/-) 4.3667603728757356e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 4 No. of parameters : 126 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:44 ETA: 00:00:00
=== Performance result === Accuracy: 50.26663331665833 (+/-) 2.2788678857122937 Testing Loss: 0.6920883044891205 (+/-) 0.007213508815540086 Precision: 0.6718153910764981 Recall: 0.5026663331665833 F1 score: 0.34017623353104365 Testing Time: 0.008905775431336731 (+/-) 0.006393594466951613 Training Time: 9.97147839208911e-07 (+/-) 3.150893875173259e-05 === Average network evolution === Total hidden node: 6.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=28, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 28 No. of nodes : 6 No. of parameters : 188 Voting weight: [1.0]
Mean Accuracy: 54.87060295221416 Std Accuracy: 5.637552435216559 Hidden Node mean 4.6 Hidden Node std: 0.7999999999999999 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [8]:
%run ADL_susy.ipynb
Number of input: 18 Number of output: 2 Number of batch: 4000 All labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:47:30 ETA: 00:00:00
=== Performance result === Accuracy: 77.98624656164041 (+/-) 2.870595926016961 Testing Loss: 0.46739256575752064 (+/-) 0.03808193857755734 Precision: 0.7820258498962392 Recall: 0.7798624656164042 F1 score: 0.7777780905095765 Testing Time: 0.01032141984537501 (+/-) 0.00653900627634962 Training Time: 0.684944398703054 (+/-) 0.021504909906513797 === Average network evolution === Total hidden node: 13.223055763940986 (+/-) 2.179207608533761 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=15, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=15, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 15 No. of parameters : 317 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:46:34 ETA: 00:00:00
=== Performance result === Accuracy: 77.67516879219805 (+/-) 3.2094666588585863 Testing Loss: 0.4738897577721824 (+/-) 0.04163854025241314 Precision: 0.7802283674036524 Recall: 0.7767516879219805 F1 score: 0.7740490196201357 Testing Time: 0.009990863664116254 (+/-) 0.006582541361882863 Training Time: 0.6713744699731413 (+/-) 0.015871658745390784 === Average network evolution === Total hidden node: 7.553138284571143 (+/-) 2.5114122175508693 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=10, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=10, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 10 No. of parameters : 212 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:46:31 ETA: 00:00:00
=== Performance result === Accuracy: 77.78319579894973 (+/-) 3.1024061501361255 Testing Loss: 0.472243248536963 (+/-) 0.039934397161484524 Precision: 0.780261644734397 Recall: 0.7778319579894973 F1 score: 0.7755870941501782 Testing Time: 0.010030536181809277 (+/-) 0.006650525102604511 Training Time: 0.670523231403325 (+/-) 0.04033957599114281 === Average network evolution === Total hidden node: 9.486871717929482 (+/-) 1.933701112192794 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=11, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=11, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 11 No. of parameters : 233 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:47:00 ETA: 00:00:00
=== Performance result === Accuracy: 77.80165041260317 (+/-) 3.0552677365584047 Testing Loss: 0.4695698034393695 (+/-) 0.03943318144787431 Precision: 0.7798153969026214 Recall: 0.7780165041260315 F1 score: 0.7760571855446129 Testing Time: 0.010456002572382292 (+/-) 0.006981026421497293 Training Time: 0.6769910026830743 (+/-) 0.05434663596325156 === Average network evolution === Total hidden node: 10.490372593148287 (+/-) 2.4437343954812203 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 13 No. of parameters : 275 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:45:13 ETA: 00:00:00
=== Performance result === Accuracy: 77.60495123780946 (+/-) 2.903258982514776 Testing Loss: 0.47470015815032307 (+/-) 0.038621717103126424 Precision: 0.7791097126541696 Recall: 0.7760495123780945 F1 score: 0.7734965691343383 Testing Time: 0.007933711612126207 (+/-) 0.0054821416210230674 Training Time: 0.6528648394708426 (+/-) 0.030866081476768156 === Average network evolution === Total hidden node: 4.51937984496124 (+/-) 1.3209838246814924 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 7 No. of parameters : 149 Voting weight: [1.0]
Mean Accuracy: 77.776008004002 Std Accuracy: 3.011797492196806 Hidden Node mean 9.05552776388194 Hidden Node std: 3.6043048866546425 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 50% labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:24:05 ETA: 00:00:00
=== Performance result === Accuracy: 77.2339584896224 (+/-) 3.467835575711796 Testing Loss: 0.48016016785578003 (+/-) 0.04312918134969722 Precision: 0.7741639762931362 Recall: 0.772339584896224 F1 score: 0.7702468284018572 Testing Time: 0.00981406069720021 (+/-) 0.006786488483253998 Training Time: 0.33417996498130803 (+/-) 0.01600848932721495 === Average network evolution === Total hidden node: 9.373093273318329 (+/-) 2.5688491500166273 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 12 No. of parameters : 254 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:24:30 ETA: 00:00:00
=== Performance result === Accuracy: 77.04901225306327 (+/-) 3.5927378614275676 Testing Loss: 0.48357151955120203 (+/-) 0.04518072342098516 Precision: 0.7725856606452249 Recall: 0.7704901225306326 F1 score: 0.7682254478584837 Testing Time: 0.010044431352531889 (+/-) 0.007039065820673812 Training Time: 0.33998667105760355 (+/-) 0.013671257857974104 === Average network evolution === Total hidden node: 9.129032258064516 (+/-) 3.0939857830500945 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 12 No. of parameters : 254 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:26:42 ETA: 00:00:00
=== Performance result === Accuracy: 77.15493873468367 (+/-) 3.520170150641851 Testing Loss: 0.48206539322030817 (+/-) 0.04455076180740554 Precision: 0.7738014381983062 Recall: 0.7715493873468368 F1 score: 0.7692367370726511 Testing Time: 0.010910514534399134 (+/-) 0.007731760963292351 Training Time: 0.3708140427960727 (+/-) 0.03059695949564086 === Average network evolution === Total hidden node: 9.129532383095773 (+/-) 3.061710155069795 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=13, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=13, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 13 No. of parameters : 275 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:30:26 ETA: 00:00:00
=== Performance result === Accuracy: 77.08737184296075 (+/-) 3.6221896877511663 Testing Loss: 0.4819852464137658 (+/-) 0.045214833976098816 Precision: 0.7735310633599096 Recall: 0.7708737184296074 F1 score: 0.7683611035727851 Testing Time: 0.011700695054058314 (+/-) 0.007948160606104471 Training Time: 0.42494460826338637 (+/-) 0.013840636385145877 === Average network evolution === Total hidden node: 8.225306326581645 (+/-) 2.6152838617168928 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=11, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=11, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 11 No. of parameters : 233 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:30:24 ETA: 00:00:00
=== Performance result === Accuracy: 77.01055263815954 (+/-) 3.4933149703452058 Testing Loss: 0.48463794790258646 (+/-) 0.04407880960482936 Precision: 0.7724010143778062 Recall: 0.7701055263815954 F1 score: 0.76773793769608 Testing Time: 0.011575422038969978 (+/-) 0.007937360275759194 Training Time: 0.4246832833763479 (+/-) 0.016911996353227876 === Average network evolution === Total hidden node: 8.436109027256814 (+/-) 2.4952781543112335 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 12 No. of parameters : 254 Voting weight: [1.0]
Mean Accuracy: 77.11328664332167 Std Accuracy: 3.519079782362714 Hidden Node mean 8.860030015007503 Hidden Node std: 2.813249968771518 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (4000 of 4000) |####################| Elapsed Time: 0:16:48 ETA: 00:00:00
=== Performance result === Accuracy: 75.57384346086523 (+/-) 4.228990079886162 Testing Loss: 0.5057052498640016 (+/-) 0.049261711691314956 Precision: 0.7580715937418158 Recall: 0.7557384346086522 F1 score: 0.7529709860923577 Testing Time: 0.009794227180852983 (+/-) 0.006852701674176533 Training Time: 0.2223502409759239 (+/-) 0.007063613873444078 === Average network evolution === Total hidden node: 3.5518879719929983 (+/-) 1.0268930999926753 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 5 No. of parameters : 107 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:16:56 ETA: 00:00:00
=== Performance result === Accuracy: 76.0894223555889 (+/-) 3.9113923849452394 Testing Loss: 0.4968418701123106 (+/-) 0.04763997052761651 Precision: 0.7635583750631517 Recall: 0.760894223555889 F1 score: 0.7581097383142541 Testing Time: 0.01149157244850916 (+/-) 0.007944791170686104 Training Time: 0.22265922710221242 (+/-) 0.006826415346526094 === Average network evolution === Total hidden node: 8.270317579394849 (+/-) 2.2600826683078297 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=12, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=12, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 12 No. of parameters : 254 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:16:47 ETA: 00:00:00
=== Performance result === Accuracy: 75.21930482620655 (+/-) 4.614118544135927 Testing Loss: 0.5103747375192091 (+/-) 0.05351065790823896 Precision: 0.7556427730784236 Recall: 0.7521930482620656 F1 score: 0.7487585678431022 Testing Time: 0.009879437289437106 (+/-) 0.006872226182543806 Training Time: 0.22209939005137028 (+/-) 0.007939917607858761 === Average network evolution === Total hidden node: 4.114778694673668 (+/-) 1.3113005079087352 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 6 No. of parameters : 128 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:14:59 ETA: 00:00:00
=== Performance result === Accuracy: 75.71702925731434 (+/-) 3.7942122487469465 Testing Loss: 0.5046080466537781 (+/-) 0.04509890771480102 Precision: 0.7608973764334855 Recall: 0.7571702925731433 F1 score: 0.753775428551792 Testing Time: 0.008114366121189568 (+/-) 0.00543185753214071 Training Time: 0.19892345216459678 (+/-) 0.02251320893838352 === Average network evolution === Total hidden node: 2.981495373843461 (+/-) 0.6799067823785002 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 3 No. of parameters : 65 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:13:54 ETA: 00:00:00
=== Performance result === Accuracy: 76.04596149037259 (+/-) 4.200841562946241 Testing Loss: 0.4973336264368116 (+/-) 0.05024681831598816 Precision: 0.7627710607412898 Recall: 0.7604596149037259 F1 score: 0.7578355555906945 Testing Time: 0.009770973350561152 (+/-) 0.00650850416899263 Training Time: 0.181366425390451 (+/-) 0.009010186260188255 === Average network evolution === Total hidden node: 12.406101525381345 (+/-) 2.8593997311325285 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=17, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=17, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 17 No. of parameters : 359 Voting weight: [1.0]
Mean Accuracy: 75.73555777888943 Std Accuracy: 4.1518268400299485 Hidden Node mean 6.265432716358179 Hidden Node std: 4.0257277531300275 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
100% (4000 of 4000) |####################| Elapsed Time: 0:01:42 ETA: 00:00:00
=== Performance result === Accuracy: 55.00765382691345 (+/-) 2.27239814840986 Testing Loss: 0.6869080970739352 (+/-) 0.0029102173211642294 Precision: 0.5679459735925508 Recall: 0.5500765382691346 F1 score: 0.5449374346959548 Testing Time: 0.009071180914687537 (+/-) 0.006767871047431125 Training Time: 1.7455603373891535e-06 (+/-) 4.167991752025205e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 4 No. of parameters : 86 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:45 ETA: 00:00:00
=== Performance result === Accuracy: 55.19619809904953 (+/-) 2.201315163033201 Testing Loss: 0.6855107150923436 (+/-) 0.011463560657816677 Precision: 0.7142886840962757 Recall: 0.5519619809904952 F1 score: 0.40431107887893375 Testing Time: 0.009241514112902856 (+/-) 0.006775168223504272 Training Time: 1.495930479430389e-06 (+/-) 3.858611974586452e-05 === Average network evolution === Total hidden node: 7.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 7 No. of parameters : 149 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:44 ETA: 00:00:00
=== Performance result === Accuracy: 55.596448224112066 (+/-) 2.1779001390567587 Testing Loss: 0.6836581360315788 (+/-) 0.005647815671891635 Precision: 0.5864567045187659 Recall: 0.5559644822411206 F1 score: 0.4405474032881035 Testing Time: 0.009159179554395882 (+/-) 0.006674665793074701 Training Time: 9.883219388319647e-07 (+/-) 3.1232935051246496e-05 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 5 No. of parameters : 107 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:43 ETA: 00:00:00
=== Performance result === Accuracy: 57.46658329164582 (+/-) 2.218057431927008 Testing Loss: 0.6817890358990941 (+/-) 0.0033350998966105608 Precision: 0.5697623597167064 Recall: 0.5746658329164582 F1 score: 0.5627502665848965 Testing Time: 0.009079338610917703 (+/-) 0.0064903633984177075 Training Time: 4.98782640221478e-07 (+/-) 2.229508443294067e-05 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 4 No. of parameters : 86 Voting weight: [1.0]
100% (4000 of 4000) |####################| Elapsed Time: 0:01:44 ETA: 00:00:00
=== Performance result === Accuracy: 54.173886943471736 (+/-) 2.2002452110432986 Testing Loss: 0.6903722378955953 (+/-) 0.004548073751486143 Precision: 0.4800594934517026 Recall: 0.5417388694347174 F1 score: 0.3836776637920759 Testing Time: 0.00899448312479833 (+/-) 0.00658753091330787 Training Time: 2.2441640742246123e-06 (+/-) 4.72460547522421e-05 === Average network evolution === Total hidden node: 3.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=18, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 18 No. of nodes : 3 No. of parameters : 65 Voting weight: [1.0]
Mean Accuracy: 55.4881861396047 Std Accuracy: 2.4689878418291893 Hidden Node mean 4.6 Hidden Node std: 1.3564659966250536 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
In [9]:
%run ADL_electricitypricing.ipynb
Number of input: 8 Number of output: 2 Number of batch: 90 All labeled
100% (90 of 90) |########################| Elapsed Time: 0:01:20 ETA: 00:00:00
=== Performance result === Accuracy: 61.2247191011236 (+/-) 7.890020448256443 Testing Loss: 0.6419403027282672 (+/-) 0.04713971598759563 Precision: 0.6034411434438038 Recall: 0.6122471910112359 F1 score: 0.6004095840415196 Testing Time: 0.003462823589196366 (+/-) 0.004530176743750663 Training Time: 0.8977906703948975 (+/-) 0.22986180136172532 === Average network evolution === Total hidden node: 13.168539325842696 (+/-) 5.34692173345883 Number of layer: 2.134831460674157 (+/-) 0.7523896199003689 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 6 No. of parameters : 68 basicNet( (linear): Linear(in_features=6, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 6 No. of nodes : 7 No. of parameters : 65 basicNet( (linear): Linear(in_features=7, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 7 No. of nodes : 7 No. of parameters : 72 basicNet( (linear): Linear(in_features=7, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 7 No. of nodes : 4 No. of parameters : 42 Voting weight: [0.0, 0.0, 0.5, 0.5]
100% (90 of 90) |########################| Elapsed Time: 0:01:35 ETA: 00:00:00
=== Performance result === Accuracy: 57.39775280898876 (+/-) 9.375967096522704 Testing Loss: 0.6833684852953708 (+/-) 0.03418520497769158 Precision: 0.5495642363078947 Recall: 0.5739775280898877 F1 score: 0.5236313536086905 Testing Time: 0.004874127634455648 (+/-) 0.0076020039914772115 Training Time: 1.0705439321110757 (+/-) 0.41139124746448186 === Average network evolution === Total hidden node: 14.898876404494382 (+/-) 6.04114161363232 Number of layer: 2.932584269662921 (+/-) 1.109689224730462 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 basicNet( (linear): Linear(in_features=5, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 5 No. of nodes : 5 No. of parameters : 42 basicNet( (linear): Linear(in_features=5, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 5 No. of nodes : 5 No. of parameters : 42 basicNet( (linear): Linear(in_features=5, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 5 No. of nodes : 6 No. of parameters : 50 Voting weight: [0.0, 0.0, 0.5, 0.5]
100% (90 of 90) |########################| Elapsed Time: 0:01:18 ETA: 00:00:00
=== Performance result === Accuracy: 59.235955056179776 (+/-) 8.564917300098735 Testing Loss: 0.656732732660315 (+/-) 0.03934369017430781 Precision: 0.5807091301182017 Recall: 0.5923595505617978 F1 score: 0.5772561537361578 Testing Time: 0.004099934288624967 (+/-) 0.005769607222842395 Training Time: 0.8799454094318861 (+/-) 0.2299380242782132 === Average network evolution === Total hidden node: 15.415730337078651 (+/-) 3.8124838957524525 Number of layer: 2.539325842696629 (+/-) 0.7940251570361686 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 basicNet( (linear): Linear(in_features=8, out_features=2, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=2, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 2 No. of parameters : 24 Voting weight: [0.0, 0.9988738376981001, 0.0011261623018998912]
100% (90 of 90) |########################| Elapsed Time: 0:00:58 ETA: 00:00:00
=== Performance result === Accuracy: 66.09213483146067 (+/-) 8.558433973088073 Testing Loss: 0.6046314025193118 (+/-) 0.08154957830912862 Precision: 0.6562793772152254 Recall: 0.6609213483146067 F1 score: 0.6529927826434858 Testing Time: 0.002175041798795207 (+/-) 0.000530156996066129 Training Time: 0.6571797413772411 (+/-) 0.10894175272648655 === Average network evolution === Total hidden node: 9.404494382022472 (+/-) 1.387901990546053 Number of layer: 1.0449438202247192 (+/-) 0.20718077432118845 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=11, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=11, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 11 No. of parameters : 123 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:01:25 ETA: 00:00:00
=== Performance result === Accuracy: 60.139325842696636 (+/-) 8.16544676633352 Testing Loss: 0.6635280599754848 (+/-) 0.028181290381156892 Precision: 0.5898345558801048 Recall: 0.6013932584269663 F1 score: 0.56666786370885 Testing Time: 0.005019276329640592 (+/-) 0.006811368915869435 Training Time: 0.9594719168845187 (+/-) 0.31628828838238154 === Average network evolution === Total hidden node: 18.348314606741575 (+/-) 9.070387828626556 Number of layer: 3.696629213483146 (+/-) 1.9740137311660135 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 basicNet( (linear): Linear(in_features=7, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 7 No. of nodes : 5 No. of parameters : 52 basicNet( (linear): Linear(in_features=5, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 5 No. of nodes : 3 No. of parameters : 26 basicNet( (linear): Linear(in_features=3, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 5 No. of parameters : 32 basicNet( (linear): Linear(in_features=5, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 5 No. of nodes : 6 No. of parameters : 50 basicNet( (linear): Linear(in_features=6, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 6 No. of nodes : 3 No. of parameters : 29 basicNet( (linear): Linear(in_features=3, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 3 No. of nodes : 4 No. of parameters : 26 Voting weight: [0.0, 0.25, 0.0, 0.0, 0.25, 0.25, 0.25]
Mean Accuracy: 60.97727272727273 Std Accuracy: 8.897560023970915 Hidden Node mean 14.354545454545455 Hidden Node std: 6.3897451416474045 Hidden Layer mean: 2.4863636363636363 Hidden Layer std: 1.4301288735779452 50% labeled
100% (90 of 90) |########################| Elapsed Time: 0:00:30 ETA: 00:00:00
=== Performance result === Accuracy: 62.94157303370787 (+/-) 7.4953290172030975 Testing Loss: 0.6295807529031561 (+/-) 0.054910337342000695 Precision: 0.6226481787598473 Recall: 0.6294157303370786 F1 score: 0.6208054037154164 Testing Time: 0.0023432661978046547 (+/-) 0.005629818400106614 Training Time: 0.33563074101223034 (+/-) 0.014089812833291747 === Average network evolution === Total hidden node: 6.584269662921348 (+/-) 0.9458245171116835 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:30 ETA: 00:00:00
=== Performance result === Accuracy: 61.791011235955054 (+/-) 9.006559912048106 Testing Loss: 0.6412126830454623 (+/-) 0.07358266071556112 Precision: 0.6122995324322156 Recall: 0.6179101123595505 F1 score: 0.6130043501683762 Testing Time: 0.001814922589934274 (+/-) 0.0005507066903755827 Training Time: 0.33537284711773474 (+/-) 0.014741258898757931 === Average network evolution === Total hidden node: 7.48314606741573 (+/-) 0.7049162760160954 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=8, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=8, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 8 No. of parameters : 90 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:30 ETA: 00:00:00
=== Performance result === Accuracy: 61.69662921348315 (+/-) 8.563581754033939 Testing Loss: 0.6343980424859551 (+/-) 0.04891472357769406 Precision: 0.6097433299276526 Recall: 0.6169662921348315 F1 score: 0.6090792955450476 Testing Time: 0.0016705079025097107 (+/-) 0.0005154053811513346 Training Time: 0.3371429979131463 (+/-) 0.014292861451494747 === Average network evolution === Total hidden node: 3.9213483146067416 (+/-) 0.8241402616131347 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:30 ETA: 00:00:00
=== Performance result === Accuracy: 62.17977528089887 (+/-) 9.243037826608091 Testing Loss: 0.6318068202961696 (+/-) 0.05921296677135695 Precision: 0.6147862369637056 Recall: 0.6217977528089887 F1 score: 0.6138174612277647 Testing Time: 0.002141151535377074 (+/-) 0.004052209544044298 Training Time: 0.3354829429240709 (+/-) 0.014906567455851696 === Average network evolution === Total hidden node: 5.51685393258427 (+/-) 0.563144453309738 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=6, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=6, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 6 No. of parameters : 68 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:30 ETA: 00:00:00
=== Performance result === Accuracy: 61.03820224719101 (+/-) 8.97376332587165 Testing Loss: 0.6400331158316537 (+/-) 0.05221618024303053 Precision: 0.6013241613235647 Recall: 0.6103820224719101 F1 score: 0.5981842367666058 Testing Time: 0.002196657523680269 (+/-) 0.003939051596741575 Training Time: 0.3349689847967598 (+/-) 0.012813952189163803 === Average network evolution === Total hidden node: 4.842696629213483 (+/-) 0.36408655609032925 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
N/A% (0 of 90) | | Elapsed Time: 0:00:00 ETA: --:--:--
Mean Accuracy: 62.05727272727273 Std Accuracy: 8.64521895291274 Hidden Node mean 5.693181818181818 Hidden Node std: 1.4330351970670705 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 25% labeled
100% (90 of 90) |########################| Elapsed Time: 0:00:16 ETA: 00:00:00
=== Performance result === Accuracy: 59.46741573033708 (+/-) 8.492849276136266 Testing Loss: 0.6631052761935117 (+/-) 0.05002489367199023 Precision: 0.5833845398751077 Recall: 0.5946741573033708 F1 score: 0.5799333748443444 Testing Time: 0.0021852321839064695 (+/-) 0.0039380650486509705 Training Time: 0.17928421363401947 (+/-) 0.009262633300922987 === Average network evolution === Total hidden node: 6.966292134831461 (+/-) 0.31620780403304033 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=7, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=7, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 7 No. of parameters : 79 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:16 ETA: 00:00:00
=== Performance result === Accuracy: 57.184269662921366 (+/-) 9.45614239322726 Testing Loss: 0.6749118640181724 (+/-) 0.04386001923439582 Precision: 0.5560947769899302 Recall: 0.5718426966292135 F1 score: 0.552415687880006 Testing Time: 0.0021293404397000086 (+/-) 0.00522034253913298 Training Time: 0.18170101187202367 (+/-) 0.010911232068642047 === Average network evolution === Total hidden node: 3.9213483146067416 (+/-) 0.2691943494541784 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:16 ETA: 00:00:00
=== Performance result === Accuracy: 60.08089887640448 (+/-) 8.45231005462039 Testing Loss: 0.648596642392405 (+/-) 0.050094802488912245 Precision: 0.5897461536882423 Recall: 0.600808988764045 F1 score: 0.5842773478583398 Testing Time: 0.002150897229655405 (+/-) 0.004899470925512456 Training Time: 0.17940275588732088 (+/-) 0.008528572788075579 === Average network evolution === Total hidden node: 4.955056179775281 (+/-) 0.3321740561594266 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:16 ETA: 00:00:00
=== Performance result === Accuracy: 61.48314606741573 (+/-) 8.573269615497498 Testing Loss: 0.6456978475109915 (+/-) 0.07813198266286593 Precision: 0.607827533470687 Recall: 0.6148314606741573 F1 score: 0.6076566513905478 Testing Time: 0.002163345894117034 (+/-) 0.003939455129168599 Training Time: 0.18150172876508047 (+/-) 0.010218377309941932 === Average network evolution === Total hidden node: 8.876404494382022 (+/-) 0.8186069932623367 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=9, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=9, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 9 No. of parameters : 101 Voting weight: [1.0]
100% (90 of 90) |########################| Elapsed Time: 0:00:16 ETA: 00:00:00
=== Performance result === Accuracy: 59.546067415730334 (+/-) 8.891190643082336 Testing Loss: 0.6552978365608816 (+/-) 0.05605277609281667 Precision: 0.5846553051414846 Recall: 0.5954606741573034 F1 score: 0.581934941171928 Testing Time: 0.0020848043848959246 (+/-) 0.004905661444225021 Training Time: 0.17938483163212124 (+/-) 0.011266079873822941 === Average network evolution === Total hidden node: 4.741573033707865 (+/-) 0.4377698817702957 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
Mean Accuracy: 59.69272727272727 Std Accuracy: 8.813261795219429 Hidden Node mean 5.920454545454546 Hidden Node std: 1.8501940371773473 Hidden Layer mean: 1.0 Hidden Layer std: 0.0 Infinite Delay
92% (83 of 90) |###################### | Elapsed Time: 0:00:00 ETA: 0:00:00
=== Performance result === Accuracy: 57.91590909090908 (+/-) 7.7106609545078495 Testing Loss: 0.6873242618008093 (+/-) 0.011148719115285035 Precision: 0.5987923240746212 Recall: 0.5791590909090909 F1 score: 0.4300044348230497 Testing Time: 0.001892832192507657 (+/-) 0.005379470576593036 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
97% (88 of 90) |####################### | Elapsed Time: 0:00:00 ETA: 0:00:00C:\Users\SCSE\AppData\Local\Continuum\miniconda3\envs\stmicro\lib\site-packages\sklearn\metrics\_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
=== Performance result === Accuracy: 42.224999999999994 (+/-) 7.689296722776998 Testing Loss: 0.7789332744750109 (+/-) 0.04732865659513843 Precision: 0.1782950625 Recall: 0.42225 F1 score: 0.25072253471611883 Testing Time: 0.0020740167661146684 (+/-) 0.005043558288129427 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 5.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=5, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=5, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 5 No. of parameters : 57 Voting weight: [1.0]
61% (55 of 90) |############## | Elapsed Time: 0:00:00 ETA: 0:00:00
=== Performance result === Accuracy: 42.85 (+/-) 7.858015594977467 Testing Loss: 0.7389877397905696 (+/-) 0.040833189883770694 Precision: 0.7039752475308084 Recall: 0.4285 F1 score: 0.2653865400173453 Testing Time: 0.0014166398481889205 (+/-) 0.004031409046678362 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
75% (68 of 90) |################## | Elapsed Time: 0:00:00 ETA: 0:00:00
=== Performance result === Accuracy: 47.96136363636364 (+/-) 9.962742318464942 Testing Loss: 0.6962939094413411 (+/-) 0.012933278094838116 Precision: 0.59579867510426 Recall: 0.47961363636363635 F1 score: 0.4089718344781493 Testing Time: 0.0017793910069899125 (+/-) 0.005384320046352537 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 4.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=4, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=4, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 4 No. of parameters : 46 Voting weight: [1.0]
97% (88 of 90) |####################### | Elapsed Time: 0:00:00 ETA: 0:00:00
=== Performance result === Accuracy: 42.220454545454544 (+/-) 7.694274126832431 Testing Loss: 0.7070060277527029 (+/-) 0.011619741677921049 Precision: 0.17828397313514252 Recall: 0.42220454545454544 F1 score: 0.25070355721750803 Testing Time: 0.0013938952576030385 (+/-) 0.00393680174874041 Training Time: 0.0 (+/-) 0.0 === Average network evolution === Total hidden node: 3.0 (+/-) 0.0 Number of layer: 1.0 (+/-) 0.0 === Final network structure === basicNet( (linear): Linear(in_features=8, out_features=3, bias=True) (activation): Sigmoid() (linearOutput): Linear(in_features=3, out_features=2, bias=True) ) No. of inputs : 8 No. of nodes : 3 No. of parameters : 35 Voting weight: [1.0]
Mean Accuracy: 46.794022988505745 Std Accuracy: 9.841589496395564 Hidden Node mean 4.0 Hidden Node std: 0.6324555320336759 Hidden Layer mean: 1.0 Hidden Layer std: 0.0
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